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D 1269 OULU 2014 UNIV ER S IT Y OF OULU P. O. BR[ 00 FI-90014 UNIVERSITY OF OULU FINLAND U N I V E R S I TAT I S S E R I E S SCIENTIAE RERUM NATURALIUM Professor Esa Hohtola HUMANIORA University Lecturer Santeri Palviainen TECHNICA Postdoctoral research fellow Sanna Taskila ACTA IMMUNE CELL INFILTRATION AND INFLAMMATORY BIOMARKERS IN COLORECTAL CANCER MEDICA Professor Olli Vuolteenaho SCIENTIAE RERUM SOCIALIUM University Lecturer Veli-Matti Ulvinen SCRIPTA ACADEMICA Director Sinikka Eskelinen OECONOMICA Professor Jari Juga EDITOR IN CHIEF Professor Olli Vuolteenaho PUBLICATIONS EDITOR Publications Editor Kirsti Nurkkala ISBN 978-952-62-0640-0 (Paperback) ISBN 978-952-62-0641-7 (PDF) ISSN 0355-3221 (Print) ISSN 1796-2234 (Online) U N I V E R S I T AT I S O U L U E N S I S Juha Väyrynen E D I T O R S Juha Väyrynen A B C D E F G O U L U E N S I S ACTA A C TA D 1269 UNIVERSITY OF OULU GRADUATE SCHOOL; UNIVERSITY OF OULU, FACULTY OF MEDICINE, INSTITUTE OF DIAGNOSTICS, DEPARTMENT OF PATHOLOGY; MEDICAL RESEARCH CENTER OULU; OULU UNIVERSITY HOSPITAL D MEDICA ACTA UNIVERSITATIS OULUENSIS D Medica 1269 JUHA VÄYRYNEN IMMUNE CELL INFILTRATION AND INFLAMMATORY BIOMARKERS IN COLORECTAL CANCER Academic dissertation to be presented with the assent of the Doctoral Training Committee of Health and Biosciences of the University of Oulu for public defence in Auditorium 4 of Oulu University Hospital, on 12 December 2014, at 12 noon U N I VE R S I T Y O F O U L U , O U L U 2 0 1 4 Copyright © 2014 Acta Univ. Oul. D 1269, 2014 Supervised by Professor Markus Mäkinen Reviewed by Professor Timo Paavonen Docent Jari Sundström Opponent Professor Ilmo Leivo ISBN 978-952-62-0640-0 (Paperback) ISBN 978-952-62-0641-7 (PDF) ISSN 0355-3221 (Printed) ISSN 1796-2234 (Online) Cover Design Raimo Ahonen JUVENES PRINT TAMPERE 2014 Väyrynen, Juha, Immune cell infiltration and inflammatory biomarkers in colorectal cancer. University of Oulu Graduate School; University of Oulu, Faculty of Medicine, Institute of Diagnostics, Department of Pathology; Medical Research Center Oulu; Oulu University Hospital Acta Univ. Oul. D 1269, 2014 University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland Abstract Colorectal cancer (CRC) is one of the most common malignancies and causes of cancer deaths in Finland. Increased number of tumor-infiltrating immune cells has been associated with improved survival in CRC. However, accurate, reproducible analysis methods, as well as better understanding of the interrelationships between different inflammatory markers would be important in order to establish a valuable prognostic and potentially predictive tool. In these studies, a computer-assisted method for the analysis of the densities of tumorinfiltrating immune cells and a quantitative method for the evaluation of CRC-associated lymphoid reaction (CLR) were adopted and validated. Utilizing the new methods, the inflammatory cell infiltration was characterized in independent groups of 418 (Cohort 1) and 149 (Cohort 2) CRC patients. Serum matrix metalloproteinase-8 (MMP-8) levels were measured in Cohort 2 and in a control group of 83 healthy age- and gender-matched controls. The automated cell counting method was found accurate and reproducible. In the tumor samples, there were high positive correlations between different types of immune cells, with the exception of mast cells and CD1a+ immature dendritic cells. High numbers of T cells predicted improved disease-free survival. High CLR density correlated with low tumor stage, but also with better survival regardless of stage. The median serum MMP-8 level of the patients was more than three times higher than that of the healthy controls. In conclusion, the present studies provide insight into the significance of various immune cell types and inflammatory markers in CRC and validate new methods for the analysis of immune cell infiltration in CRC. The results suggest that, especially, the densities of tumor-infiltrating T cells and CLR represent relevant prognostic indicators in CRC. Further studies are needed to evaluate the potential value of serum MMP-8 as an aid for CRC diagnostics, surveillance, or prognostication. Keywords: colorectal cancer, computer-assisted image analysis, immunohistochemistry, inflammation, prognosis, tumor immunology Väyrynen, Juha, Paikallinen tulehdussolukko ja tulehdusmerkkiaineet kolorektaalisyövässä. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Lääketieteellinen tiedekunta, Diagnostiikan laitos, Patologia; Medical Research Center Oulu; Oulun yliopistollinen sairaala Acta Univ. Oul. D 1269, 2014 Oulun yliopisto, PL 8000, 90014 Oulun yliopisto Tiivistelmä Kolorektaalisyöpä on yksi yleisimmistä pahanlaatuisista kasvaintaudeista ja syöpäkuolemien aiheuttajista Suomessa. Tulehdussolujen korkean määrän kasvainnäytteissä on havaittu olevan yhteydessä potilaiden parempaan ennusteeseen. Tarkat ja luotettavat analyysimenetelmät sekä tieto eri tulehdusmerkkiaineiden keskinäisistä yhteyksistä olisivat tärkeitä, jotta tulehdussolukon määritystä voitaisiin luotettavasti käyttää potilaiden ennusteen arviointiin. Tutkimuksessa otettiin käyttöön ja validoitiin uusi tietokonepohjainen menetelmä kasvaimen tulehdussolukon arviointiin sekä uusi menetelmä kolorektaalisyövän imukeräsreaktion arviointiin. Kasvainnäytteiden tulehdussolukon määrää ja laatua analysoitiin itsenäisissä 418 (Kohortti 1) ja 149 (Kohortti 2) kolorektaalisyöpäpotilaan aineistoissa uusia menetelmiä hyödyntäen. Lisäksi kohortilta 2 sekä 83 terveeltä ikä- ja sukupuolivalikoidulta verrokilta määritettiin seerumin matriksin metalloproteinaasi-8 (MMP-8) -taso. Tietokonepohjaisen kuva-analyysin tarkkuus ja toistettavuus todettiin erinomaiseksi. Kasvainnäytteistä analysoitujen tulehdussolutyyppien määrät olivat riippuvaisia toisistaan mastsoluja ja CD1a+ epäkypsiä dendriittisoluja lukuun ottamatta. T-solujen runsas määrä oli yhteydessä taudin vähäisempään uusiutumisriskiin. Korkea imukerästiheys kasvainnäytteissä oli yhteydessä matalaan levinneisyysasteeseen sekä potilaiden parempaan ennusteeseen levinneisyysasteesta riippumatta. Seerumin MMP-8-tason mediaani oli potilailla yli kolme kertaa korkeampi kuin terveillä verrokeilla. Tutkimus tuo lisätietoa eri tulehdussolutyyppien ja tulehdusmerkkiaineiden merkityksestä kolorektaalisyövässä, ja sen tuloksena validoitiin uusia tulehdussolukon analysointimenetelmiä. Tulosten perusteella erityisesti kasvaimen alueen T-solujen ja imukerästen tiheys tuovat hyödyllistä tietoa potilaiden ennusteesta. Lisätutkimuksia tarvitaan seerumin MMP-8:n mahdollisesta soveltuvuudesta kolorektaalisyövän diagnostiikan, seurannan tai ennusteen määrittämisen apuvälineeksi. Asiasanat: ennuste, immunohistokemia, kasvainimmunologia, kolorektaalisyöpä, tietokoneavusteinen kuva-analyysi, tulehdus To Sara 8 Acknowledgements This study was carried out at the Department of Pathology, Institute of Diagnostics, University of Oulu from 2010 to 2014. I wish to thank the Head of the Department, Professor Tuomo Karttunen, MD, PhD, for the opportunity to work at the Department of Pathology. Tuomo is also greatly acknowledged for his wise comments and support during these years. With his expertise on gastrointestinal pathology, he has made a significant contribution to the completion of the thesis. I wish to express my deepest gratitude to my supervisor, Professor Markus Mäkinen, MD, PhD, for his guidance and advice on both research and life in general during these years. Without his vast knowledge and optimism, the outcome of this thesis could not have been as good as it was. Markus has created a humane atmosphere for his research group, which I also greatly value. I appreciate and thank the official pre-examiners of this dissertation, Professor Timo Paavonen, MD, PhD, and Docent Jari Sundström, MD, PhD, for their insightful comments that have made a notable improvement in the manuscript. I also thank Anna Vuolteenaho, MA, for her excellent linguistic editing of the manuscript. I wish to express my warmest thanks to the members of our research group, Tiina Kantola, MSc, Anne-Mari Moilanen, PhD, Päivi Sirniö, MSc, Anne Tuomisto, PhD, and Riitta Vuento, for their collaboration. Anne is, specifically, acknowledged for her inspiring comments and guidance. I am also deeply grateful to Riitta for her expertise and vast efforts in the preparation of the study material, as well as for her friendship. For significant collaboration and participation in this work I also want to acknowledge Risto Bloigu, MSc, Jan Böhm, MD, PhD, Professor Karl-Heinz Herzig, MD, PhD, Toni Karhu, MSc, Kai Klintrup, MD, PhD, Professor Jyrki Mäkelä, MD, PhD, Professor Tuula Salo, DDS, PhD, Professor Timo Sorsa, DDS, PhD, Taina Tervahartiala, DDS, PhD, and Juha Vornanen, MD. Tuula is, especially, acknowledged for her cheerful encouragement in the early stages of my work. Chief Department Physicians, Docent Paavo Pääkkö, MD, PhD, and Docent Helena Autio-Harmainen, MD, PhD, are thanked for giving me great facilities to work at the Oulu University Hospital. I would also like to acknowledge other pathologists and residents working in the hospital for their support and guidance during these years. 9 My thanks are also owed to all the staff at the Department of Pathology for their encouragement and for the laboratory and technical assistance. The teaching staff, Docent Kirsi-Maria Haapasaari, MD, PhD, Johanna Mäkinen, MD, and Vesa-Matti Pohjanen, MD, are, especially, thanked for their friendship. I cannot thank enough my parents, Raija and Eero, who have supported me and taught me critical and logical thinking from an early age. I wish to thank my brother Henri for his friendship and for the hard workouts during these years. I also wish to thank all my friends for taking my thoughts away from research every now and then (and Jaakko Kangas also for stimulating research-related conversations). Above all, my thanks are owed to my wonderful wife and co-researcher Sara for her continuous love and support. We have had the most enjoyable moments both in and out of work that have given me the drive to push forward with this research with a fruitful outcome. I acknowledge the financial support for this thesis provided by the Academy of Finland, Emil Aaltonen Foundation, Finnish Cancer Foundation, Finnish Medical Foundation, Northern Finland Cancer Foundation, Orion-Farmos Research Foundation, Oulu University Scholarship Foundation, and Vatsatautien tutkimussäätiö. Oulu, October 2014 10 Juha Väyrynen Abbreviations AACR APC BMI BRAF CD CEA CIMP CIN CLR CRC CRP CRT CSS CT CT-S DAB DC DFS DNA ECM EGFR EMT ESMO e.g. FoxP3 GPS H&E HP IBD IEL IFMA IFN IM iNOS IL American Association for Cancer Research adenomatous polyposis coli body mass index v-raf murine sarcoma viral oncogene homolog B1 cluster of differentiation carcinoembryonic antigen CpG island methylator phenotype chromosomal instability colorectal cancer associated lymphoid reaction colorectal cancer C-reactive protein chemoradiotherapy cancer-specific survival center of tumor center of tumor, stromal 3,3'-diaminobenzidine dendritic cell disease-free survival deoxyribonucleic acid extracellular matrix epidermal growth factor receptor epithelial to mesenchymal transition European Society for Medical Oncology exempli gratia forkhead box P3 Glasgow prognostic score hematoxylin and eosin hyperplastic polyp inflammatory bowel disease intraepithelial immunofluorometric assay interferon invasive margin inducible nitric oxide synthase interleukin 11 i.e. id est KRAS Kirsten rat sarcoma viral oncogene homolog LOH loss of heterozygosity MAPK/ERK mitogen-activated protein kinases/ extracellular signal-regulated kinases MCA methyl cyanoacrylate MLH MutL homolog MMP matrix metalloproteinase MMR mismatch repair MPO myeloperoxidase MSH MutS homolog MSI microsatellite instability MSS microsatellite stability MYD88 myeloid differentiation primary response gene 88 NF-κB nuclear factor-κB NSAID non-steroidal anti-inflammatory drug OS overall survival ROC receiver operating characteristics ROS reactive oxygen species SSA sessile serrated adenoma STAT3 signal transducer and activator of transcription 3 TAA tumor-associated antigen TAM tumor-associated macrophage TGFβR2 transforming growth factor beta receptor 2 Th cell T helper cell TIMP tissue inhibitor of metalloproteinases TLR Toll-like receptor TNF tumor necrosis factor TNM tumor, node, metastasis TP53 tumor protein p53 TReg cell regulatory T cell TSA traditional serrated adenoma RAG-2 recombination-activating gene 2 RNA ribonucleic acid RT radiotherapy VEFG vascular endothelial growth factor WHO World Health Organization 12 List of original publications This thesis is based on the following publications, which are referred to throughout the text by their Roman numerals: I Väyrynen JP, Vornanen JO, Sajanti S, Böhm JP, Tuomisto A, & Mäkinen MJ (2012) An improved image analysis method for cell counting lends credibility to the prognostic significance of T cells in colorectal cancer. Virchows Arch. 460(5): 455–465. II Väyrynen JP, Tuomisto A, Klintrup K, Mäkelä J, Karttunen TJ, & Mäkinen MJ (2013) Detailed analysis of inflammatory cell infiltration in colorectal cancer. Br. J. Cancer 109(7): 1839–1847. III Väyrynen JP, Sajanti SA, Klintrup K, Mäkelä J, Herzig K-H, Karttunen TJ, Tuomisto A, & Mäkinen MJ (2014) Characteristics and significance of colorectal cancer associated lymphoid reaction. Int. J. Cancer 134(9): 2126–35. IV Väyrynen JP, Vornanen J, Tervahartiala T, Sorsa T, Bloigu R, Salo T, Tuomisto A, & Mäkinen MJ (2012) Serum MMP-8 levels increase in colorectal cancer and correlate with disease course and inflammatory properties of primary tumors. Int. J. Cancer 131(4): E463–74. 13 14 Contents Abstract Tiivistelmä Acknowledgements 9 Abbreviations 11 List of original publications 13 Contents 15 1 Introduction 17 2 Review of the literature 19 2.1 Colorectal cancer epidemiology .............................................................. 19 2.1.1 Incidence ...................................................................................... 19 2.1.2 Risk factors ................................................................................... 19 2.2 Colorectal cancer classification ............................................................... 20 2.3 Colorectal cancer pathogenesis ............................................................... 20 2.3.1 Genomic and epigenetic mechanisms ........................................... 21 2.3.2 Morphological developmental pathways ...................................... 24 2.3.3 Intratumor heterogeneity and cancer stem cells ........................... 27 2.3.4 Invasion and metastasis ................................................................ 28 2.3.5 Immune system and inflammation ............................................... 31 2.3.6 Angiogenesis ................................................................................ 39 2.4 Colorectal cancer diagnosis and screening .............................................. 39 2.5 Colorectal cancer prognostic and predictive markers ............................. 40 2.5.1 Clinical and histopathological prognostic factors......................... 40 2.5.2 Inflammation-based prognostic markers ...................................... 46 2.5.3 Genetic prognostic and predictive markers .................................. 54 2.5.4 Blood and serum prognostic markers ........................................... 56 2.6 Colorectal cancer treatment..................................................................... 57 2.6.1 Surgical treatment ......................................................................... 57 2.6.2 Neoadjuvant treatment for rectal cancer ....................................... 58 2.6.3 Adjuvant treatment for colorectal cancer ..................................... 59 3 Aims of the study 61 4 Materials and methods 63 4.1 Patients (I-IV) ......................................................................................... 63 4.2 Control group (IV) .................................................................................. 64 4.3 Histopathological analysis (I-IV) ............................................................ 65 4.3.1 Stage and Grade (I-IV) ................................................................. 65 15 4.3.2 Necrosis (IV) ................................................................................ 65 4.3.3 Tumor budding (I) ........................................................................ 65 4.3.4 Peritumoral inflammatory reaction (II-IV) ................................... 65 4.3.5 Colorectal cancer associated lymphoid reaction (III-IV).............. 66 4.4 Immunohistochemistry (I-IV) ................................................................. 66 4.4.1 Tissue microarray (II, III) ............................................................. 66 4.4.2 Protocols (I-IV) ............................................................................ 67 4.4.3 Analysis of Immunohistochemistry (I-IV) ................................... 67 4.5 Serum analyses (IV) ................................................................................ 71 4.6 Measurement of intra- and inter-observer variation (I, III) ..................... 71 4.7 Statistical analyses (I-IV) ........................................................................ 71 5 Results 73 5.1 New methods for the evaluation of immune cell reaction ....................... 73 5.1.1 Computer-based immune cell counting ........................................ 73 5.1.2 CLR density .................................................................................. 73 5.2 Immune cell infiltration in colorectal cancer .......................................... 74 5.2.1 Characteristics of immune cell infiltration ................................... 74 5.2.2 Interrelationships between different immune cell types ............... 76 5.2.3 Relationships between immune cell infiltration and clinical and pathological variables ............................................... 77 5.2.4 Prognostic value ........................................................................... 79 5.3 Systemic inflammatory biomarkers in colorectal cancer ........................ 80 5.3.1 Serum MMP-8 .............................................................................. 80 5.3.2 Other markers ............................................................................... 80 6 Discussion 81 6.1 New methods for the evaluation of immune cell reaction ....................... 81 6.1.1 Computer-based immune cell counting ........................................ 81 6.1.2 CLR density .................................................................................. 83 6.2 Immune cell infiltration in colorectal cancer .......................................... 84 6.2.1 T cells in colorectal cancer ........................................................... 84 6.2.2 Dendritic cells in colorectal cancer............................................... 85 6.2.3 Colorectal cancer associated lymphoid reaction ........................... 85 6.2.4 Future perspectives ....................................................................... 86 6.3 Systemic inflammatory biomarkers in colorectal cancer ........................ 88 7 Conclusions 91 References 93 Original publications 123 16 1 Introduction Colorectal cancer (CRC) is one of the most common malignancies and causes of cancer deaths in the Western world (Siegel et al. 2013). TNM staging is used in the prognostic classification (Sobin & Wittekind 2002). In patients operated on in the 1990s, five-year overall survival was 65%, ranging from 90% in stage I to less than 10 % in stage IV (O’Connell et al. 2004). However, molecular heterogeneity of the disease warrants the search for additional, complementary prognostic markers (Jass 2007b). Over the past decade, the immune system has increasingly been acknowledged as an important contributor to cancer pathogenesis (Hanahan & Weinberg 2011, Schreiber et al. 2011). It has been shown that immune cells can establish an anti-tumor immune response (Koebel et al. 2007, Shankaran et al. 2001), and accordingly, numerous studies have associated increased immune cell infiltration in CRC with better disease outcome (Roxburgh & McMillan 2012). Especially, T cell infiltration has been associated with stage-independent prognostic value (Galon et al. 2006, Pagès et al. 2005), and there is an international initiative to incorporate the quantification of tumor-infiltrating T cells into cancer classification (Galon et al. 2012, 2014). However, accurate, reproducible analysis methods, as well as better understanding of the interrelationships between different inflammatory markers would be important in order to establish a valuable prognostic and potentially predictive tool. In addition to tumor-infiltrating immune cells, systemic inflammatory biomarkers and hematological parameters have also been shown to be able to predict survival in CRC (Roxburgh & McMillan 2010). Especially, the Glasgow prognostic score (GPS), consisting of serum levels of C-reactive protein (CRP) and albumin, has been found to have strong prognostic value in several independent cohorts (McMillan 2013). However, no serum prognostic markers are currently regularly used in clinical work (Sturgeon et al. 2008). Serum markers that would facilitate early detection and diagnosis of CRC would also be valuable. Matrix metalloproteinases (MMPs) form a family of zincdependent endoproteases participating in extracellular matrix (ECM) degradation. Serum levels of MMP-9 and tissue inhibitor of metalloproteinases 1 (TIMP-1) have been found to be increased in CRC and have been proposed as potential markers to aid the diagnosis of CRC (Hurst et al. 2007, Mroczko et al. 2010). MMP-8 is regarded as an important regulator of immune responses (Van Lint & Libert 2006) thanks to its capability to cleave several inflammatory mediators, 17 including CXCL5, CXCL8, CXCL9, and CCL2 (Van Lint & Libert 2007). However, its serum levels and potential function in CRC had not been studied. The aims of this work were to develop and validate accurate and reproducible methods for the analysis of immune cell infiltration in CRC and to enlighten the significance of various immune cell types and inflammatory markers in CRC. Specific points of interest were the applicability of a color layer separation based image analysis method to counting immune cells in CRC (I), the interrelationships between different inflammatory cell types within colorectal tumors (II), the characteristics and the significance of colorectal cancer associated lymphoid reaction (CLR) (III), and the value of serum MMP-8 in discriminating the CRC patients from healthy controls (IV). 18 2 Review of the literature 2.1 Colorectal cancer epidemiology 2.1.1 Incidence CRC is one of the most common malignancies and causes of cancer deaths in the Western world (Siegel et al. 2013). In patients operated on in the 1990s, five-year overall survival was 65% (O’Connell et al. 2004). In Finland, more than 2,800 new cases were diagnosed in 2011, and the incidence was third highest after breast cancer and prostate cancer (Finnish Cancer Registry 2013). Lifetime risk of CRC is about 5% in the population in industrialized countries (Siegel et al. 2013). CRC is rare in people under 40 years of age, and most patients are over 70 years of age at the time of the diagnosis (Siegel et al. 2013). 2.1.2 Risk factors The majority of CRC is sporadic. The differences in the incidence between countries around the world (Siegel et al. 2013) as well as immigrant studies (Dunn 1975, Kune et al. 1986, Shimizu et al. 1987) suggest that environmental factors account for the majority of the disease risk. Of the dietary factors, high consumption of red meat (Larsson & Wolk 2006) and heavy alcohol use (Fedirko et al. 2011) have convincingly been associated with an increased CRC risk, whereas a high intake of dietary fiber has been associated with a reduced CRC risk (Aune et al. 2011). Other known risk factors for CRC include smoking (Raimondi et al. 2008), overweight (Larsson & Wolk 2007), low level of physical activity (Samad et al. 2005), and inflammatory bowel diseases (IBDs) (Eaden et al. 2001, von Roon et al. 2007). The use of non-steroidal anti-inflammatory drugs (NSAIDs) decreases the CRC risk (Din et al. 2010). About 5% of CRCs arise in patients with a characterized germline mutation (Kwak & Chung 2007), although twin studies have suggested that genetic factors could account for up to 35% of the CRC risk (Lichtenstein et al. 2000). The most common of the hereditary colorectal cancer syndromes are familial adenomatous polyposis (FAP), with a germline mutation in adenomatous polyposis coli (APC) tumor suppressor gene (Groden et al. 1991, Nishisho et al. 1991), and Lynch syndrome, with a germline mutation in one of the mismatch repair (MMR) 19 systems of deoxyribonucleicacid (DNA) (Bronner et al. 1994, Fishel et al. 1993, Leach et al. 1993, Papadopoulos et al. 1994). 2.2 Colorectal cancer classification CRC is defined by the invasion of tumor cells through muscularis mucosae to submucosa (Hamilton et al. 2010). Adenocarcinomas, originating from the glandular epithelium, account for the vast majority of CRC (Hamilton et al. 2010, Kang et al. 2007). Several histopathological subtypes of colorectal carcinomas can be distinguished (Table 1). Other malignant colorectal tumors include neuroendocrine tumors, gastrointestinal stromal tumors (GISTs) and lymphomas (Hamilton et al. 2010). Table 1. Histopathological subtypes of colorectal carcinoma. Classification Designating features Adenocarcinoma, not otherwise Glandular differentiation specified Mucinous adenocarcinoma > 50% of the lesion is composed of extracellular mucin Signet-ring cell carcinoma Presence of > 50% of tumor cells with prominent intracytoplasmic mucin Serrated adenocarcinoma Epithelial serrations, low nucleus-to-cytoplasm ratio, clear or eosinophilic cytoplasm Micropapillary adenocarcinoma Tumor cells growing in papillary structures, which lack fibrovascular cores Medullary carcinoma Sheets of malignant cells with vesicular nuclei, prominent nucleoli, and abundant eosinophilic cytoplasm; prominent infiltration by intraepithelial lymphocytes Adenosquamous carcinoma Areas of glandular and squamous differentiation Undifferentiated carcinoma Lack of morphological, immunohistochemical, and molecular biology evidence of differentiation beyond that of an epithelial tumor Classification and designating features adapted from Hamilton et al. 2010. 2.3 Colorectal cancer pathogenesis The hallmarks of cancer include sustaining proliferative signaling, evading growth suppressors, activating invasion and metastasis, enabling replicative immortality, inducing angiogenesis, resisting cell death, deregulating cellular energetics, and avoiding immune destruction (Hanahan & Weinberg 2011). 20 Colorectal tumors acquire these traits in the multi-step process of carcinogenesis (Fearon & Vogelstein 1990). The importance of alterations in oncogenes and tumor suppressor genes was evident by the 1990s (Vogelstein et al. 1988), while the significance of epigenetic changes was highlighted in the late 1990s and early 2000s (Herman et al. 1998, Jones & Laird 1999). The research in the past decade has further highlighted the significance of tumor-host interactions during the process of carcinogenesis (Hanahan & Weinberg 2011). 2.3.1 Genomic and epigenetic mechanisms Oncogenes and tumor suppressor genes Oncogenes are defined as genes that promote tumor initiation or progression (Croce 2008). The human genome contains several proto-oncogenes that can be transformed into oncogenes by point mutations, chromosome translocations or gene amplification, which cause an increase in their expression or an alteration in the structure of their protein product (Croce 2008, Nishimura & Sekiya 1987). The products of oncogenes can be classified into six groups (Table 2). Table 2. Classification of oncogenes. Classification Function Transcription factor Controls ribonucleic acid (RNA) synthesis Chromatin remodeler Controls epigenetic alterations of DNA, thus modulating RNA synthesis Growth factor Stimulates cell growth Growth factor receptor Mediates growth factor signaling Signal transducer Transmits an extracellular signal into a functional change within the cell Apoptosis regulator Controls programmed cell death Classification adapted from Croce 2008. While oncogenes mostly increase tumor cell proliferation, the products of tumor suppressor genes act to inhibit cell proliferation (Weinberg 1991). In addition to ‘gatekeeper’ genes, forming a network of proteins that prevent uncontrolled growth, the family of tumor suppressor genes includes ‘caretaker’ genes, maintaining the integrity of the genome (Kinzler & Vogelstein 1997). Usually, one functioning allele of a tumor suppressor gene is enough to sustain its normal function, and thus, inactivation of both alleles is required for loss-of-function, 21 which is known as the ‘two-hit hypothesis’ (Knudson 1971). Table 3 presents a group of oncogenes and tumor suppressor genes commonly associated with CRC pathogenesis. Table 3. Oncogenes and tumor suppressor genes commonly associated with CRC pathogenesis. Gene Significance of the gene product References Activation of MAPK-ERK signal Bos et al. 1987 Oncogenes KRAS transduction, inhibition of apoptosis, promotion of cell survival BRAF Activation of MAPK-ERK signal Davies et al. 2002 transduction, inhibition of apoptosis, promotion of cell survival β-catenin Activation of Wnt signaling that regulates Morin et al. 1997 cell proliferation and invasion Tumor suppressor genes APC Inhibition of Wnt signaling via degrading β- Morin et al. 1997 catenin TP53 Cell cycle regulation Baker et al. 1990 TGFβR2 Receptor that is responsible for TGFβ Markowitz et al. 1995 pathway signaling mediating growth arrest and apoptosis SMAD2 and -4 Important component of TGFβ pathway Thiagalingam et al. 1996 signaling mediating growth arrest and apoptosis MLH1, MSH2, and MLH6 Enzymes contributing to DNA mismatch Fishel et al. 1993, Herman et repair and maintaining the stability of DNA al. 1998, Miyaki et al. 1997, microsatellites Papadopoulos et al. 1994, Strand et al. 1993 Modified from Markowitz & Bertagnolli 2009. Genomic and epigenetic instability Genomic and epigenetic instabilities exist in human cancers, enabling the cancer cells to acquire a sufficient amount of genetic changes to generate a malignant tumor (Issa 2004, Lengauer et al. 1998, Loeb 1991). Chromosomal instability (CIN), defined as cell-to-cell variability of gain or loss of whole chromosomes or fractions of chromosomes (Geigl et al. 2008), is the most prevalent form of genomic instabilities in CRC, present in the majority of cases (Lengauer et al. 22 1997). In about 15% of CRC, the mismatch repair (MMR) system of DNA is deficient, resulting in microsatellite instability (MSI) (Boland & Goel 2010, Boland et al. 1998). CIN and MSI can be detected in CRC precursor lesions, adenomas, indicating that genomic destabilization is an early step in CRC development (Shih et al. 2001, Stoler et al. 1999). The mechanisms underlying CIN include defects in chromosome cohesion, mitotic checkpoint function, centrosome copy number, and cell-cycle regulation (Thompson et al. 2010). Aneuploidy is an effective way to inactivate the functioning allele of tumor suppressor genes (loss of heterozygosity; LOH), such as APC and TP53, and is often present in chromosomally unstable CRCs (Fearon & Vogelstein 1990). DNA MMR is a process that has been highly conserved during evolution, and comprises homologs of bacterial MutS (MSH) and MutL (MLH) enzymes. It is responsible of strand-specific recognition and correction of mispaired bases that arise during DNA replication (Modrich & Lahue 1996). A defective MMR system results in microsatellite instability (MSI), defined as insertions or deletions in DNA microsatellite repeat sequences (Boland et al. 1998, Strand et al. 1993, Umar et al. 1994). MSI is further classified into MSI-high (MSI-H) with alterations in 30% or more of the studied markers and MSI-low (MSI-L) with alterations in less than 30% of the studied markers (Boland et al. 1998). CRCs with MSI are characterized by mutations in specific tumor suppressor genes containing microsatellites, e.g., TGFβR2 (Kim et al. 2013). Lynch syndrome is an autosomal dominant disease with a germline mutation in MSH2 or MLH1 (Bronner et al. 1994, Fishel et al. 1993, Leach et al. 1993, Papadopoulos et al. 1994), or less frequently, in other MMR enzymes such as MSH6 (Miyaki et al. 1997). MMR deficiency arises according to Knudson’s twohit hypothesis (Knudson 1971), so that the loss of wild-type allele is required to cause a phenotypic effect (Hemminki et al. 1994). The average age of CRC onset in Lynch syndrome is about 45 years (Lynch et al. 2008). Lynch syndrome represents less than 5% of CRC (Kwak & Chung 2007), while the majority of MMR deficiency and MSI in CRC results from epigenetic modifications (Herman et al. 1998, Kane et al. 1997). DNA methylation is a post-replication modification, which is typically found in cytosines that are part of the dinucleotide sequence CpG (Jaenisch & Bird 2003). About half of all genes have a CpG-rich promoter but most of the promoter CpG islands are normally unmethylated (Issa 2004). However, a group of CRCs present with a CpG island methylator phenotype (CIMP) (Toyota et al. 1999), 23 resulting in inactivation of specific caretaker and gatekeeper genes, often including MLH1 (Herman et al. 1998, Kane et al. 1997, Veigl et al. 1998). Thus, in sporadic CRC, epigenetic instability and MSI are often closely connected (Goel et al. 2007, Weisenberger et al. 2006). Like MSI, CIMP can also be classified into CIMP-low (CIMP-L) and CIMP-high (CIMP-H) according to its extent (Ogino et al. 2006). 2.3.2 Morphological developmental pathways Several models have been developed to mirror the development of CRC (Fearon & Vogelstein 1990, Mäkinen 2007). A study that analyzed the sequences of 20,857 transcripts from 18,191 human genes in 11 CRCs found that each individual cancer contains about 80 amino acid-altering mutations that are absent in normal cells (Wood et al. 2007). It is not possible that such a high number of genetic changes occur in the same order in each tumor. Indeed, over the past decades, it has become evident that CRCs form a morphologically and genetically heterogeneous group that develops via multiple pathways (Jass 2007a). The form of genomic and epigenetic instability (CIN, MSI, and CIMP) the tumor cells possess is considered one of the most important factors for the categorization of CRCs, since different forms of genetic instability are accompanied by specific genetic changes that are rare in other types (Goel et al. 2007, Kim et al. 2013). One of the most established classifications compartmentalizes CRCs based on the presence of MSI (or microsatellite stability, MSS), CIMP, and BRAF and KRAS mutations: (1) CIMP-H/MSIH/BRAF mutation; (2) CIMP-H/MSI-L or MSS/BRAF mutation; (3) CIMPL/MSS or MSI-L/KRAS mutation; (4) CIMP-neg/MSS; and (5) CIMP-neg/MSIH (Jass 2007a). The genetic properties of the tumors are also reflected by their morphology. Precursor lesions The majority of CRCs arise from adenomas that are premalignant tumors of epithelial tissue with glandular origin (Fearon & Vogelstein 1990, Jackman & Mayo 1951, Muto et al. 1975). Macroscopically, the vast majority of adenomas have been considered to be polypoid, but studies utilizing dyes such as indigo carmine have indicated that about third of adenomas may be flat or depressed (Rembacken et al. 2000, Saitoh et al. 2001). Microscopically, traditional 24 adenomas can be grouped into tubular, villous, and tubulovillous (Hamilton et al. 2010, Shinya & Wolff 1979). The designating feature present in all traditional adenomas is dysplasia of the epithelium which can be classified into low-grade and high-grade (Hamilton et al. 2010). Serrated polyps form a group of lesions characterized by a sawtooth-like infolding of the surface and crypt epithelium (Table 4) (Mäkinen 2007, Snover et al. 2010). The group includes lesions with variable malignant potential, the best characterized of which are hyperplastic polyp (HP), sessile serrated adenoma (SSA), and traditional serrated adenoma (TSA). HPs are the most common serrated lesions, characterized by serrations confined to the upper parts of the crypts (Snover et al. 2010). Small, distal HPs are considered innocuous lesions, but large ones arising in the proximal colon may resemble SSAs and harbor malignant potential (Goldstein et al. 2003, Lin et al. 2005). HPs and SSAs do not generally show dysplasia, although SSAs may develop it with progression towards carcinoma (Lash et al. 2010). Conversely, TSAs often present with dysplasia (Torlakovic et al. 2008). Table 4. Benign and premalignant epithelial tumors of the colon and rectum. Classification Designating features Traditional adenomas Presence of dysplastic epithelium Tubular adenoma Tubular glands Villous adenoma Leaf- or fingerlike projections of the epithelium overlying lamina propria Tubulovillous adenoma Serrated polyps Hyperplastic polyp Mixture of tubular and villous components; villous component 25–75% Sawtooth-like infolding of the surface and crypt epithelium Serrations confined to the upper parts of the crypts, no cytological atypia Sessile serrated adenoma Distortion of the normal crypt architecture: dilated and T- or L-shaped crypts, alterations in the position of proliferative zone; vesicular nuclei Traditional serrated adenoma Ectopic crypt formation (ECF); cytological atypia Classification and designating features adapted from Hamilton et al. 2010, Mäkinen 2007, Torlakovic et al. 2008, Snover et al. 2010. Adenoma-carcinoma pathway Vogelstein and co-workers were first to describe a model of genetic changes occurring in colorectal carcinogenesis (Fearon & Vogelstein 1990, Vogelstein et al. 1988). Although later studies have indicated that CRC is a heterogeneous 25 disease and there is a need for alternative developmental pathways (Jass 2007a), the model still represents a valuable portrayal of the typical development of the most common form of sporadic CRCs (CIMP-neg/MSS). One of the first events in the development of CIMP-neg/MSS CRCs is usually the inactivation of APC tumor suppressor that is present in the majority of sporadic CRCs and tubular adenomas (Fig. 1) (Powell et al. 1992). It is considered to have an important role in the initiation of CIN (Fodde et al. 2001). Also the activation of KRAS oncogene mostly occurs in early adenomas (Vogelstein et al. 1988), contributing to the activation of multiple intracellular signal pathways, including the MAPK/ERK pathway. Chromosome 18q loss, generally manifesting as the third step after APC loss and KRAS activation, leads to the losses of SMAD4 and SMAD2 and therefore to the abolishment of TGF-β signaling (Hahn et al. 1996, Thiagalingam et al. 1996). Mutations in TP53 tumor suppressor gene are common in diverse types of human cancers (Hollstein et al. 1991). In colorectal carcinogenesis, the inactivation of TP53 generally occurs as a late event (Baker et al. 1990). Normal epithelium Early adenoma APC inactivation KRAS mutation Carcinoma Late adenoma 18q LOH TP53 inactivation Progressive chromosomal instability Fig. 1. Genetic changes commonly associated with the pathogenesis of CRC with chromosomal instability. Modified from Fearon 2011, Kinzler & Vogelstein 1996. Serrated pathway During the past fifteen years, the malignant potential of serrated polyps has been established (Goldstein et al. 2003, Mäkinen et al. 2001). The serrated route from SSA to serrated adenocarcinoma is characterized by CIMP and MSI (Fig. 2) (Mäkinen 2007), and the majority of sporadic CRCs with MSI-H are considered to follow it (Jass 2007a). An early change in the route, present in more than 70% of SSAs (Spring et al. 2006), is BRAF mutation, most commonly BRAF V600E, 26 which results in a constitutively activated enzyme and the activation of the mitogen-activated protein kinases/extracellular signal-regulated kinases (MAP/ERK) signaling pathway. Conversely, TSAs frequently carry KRAS mutations and show MSS or MSI-L (O’Brien et al. 2006). The position of TSA in the serrated pathway of CRC is controversial. However, it has recently been shown that about 50% of endoscopically removed TSAs are accompanied by surrounding lesions with features of HP or SSA, indicating that a proportion of TSAs may develop from these lesions (Kim et al. 2013). Normal epithelium SSA BRAF mutation Serrated carcinoma SSA with dysplasia MLH1 inactivation TGFβRII inactivation Progressive DNA methylation Progressive microsatellite instability Fig. 2. Genetic changes commonly associated with the pathogenesis of serrated colorectal adenocarcinoma. Modified from Mäkinen 2007, Snover 2011. 2.3.3 Intratumor heterogeneity and cancer stem cells Intratumor heterogeneity is a phenomenon characterized by regions and cells with diverse genetic and epigenetic changes, morphology, and behavior within a single tumor and its metastases (Almendro et al. 2013). The phenomenon was highlighted by a recent study utilizing whole-exome sequencing of biopsy samples taken from different tumor areas and metastases from patients with renal cell carcinoma, which showed that 63–69% of somatic mutations were not detectable across every tumor region (Gerlinger et al. 2012). Accordingly, also CRC has been shown to present with heterogeneity within the primary tumors and between primary tumors and metastases in, e.g., activating mutations of KRAS (Baldus et al. 2010). Intratumor heterogeneity may represent a challenge for personalized medicine and biomarker development. Accumulating evidence suggests that not all tumor cells possess equal ability to proliferate. Cancer stem cells were defined as “cells within a tumor that possess 27 the capacity to self-renew and to cause the heterogeneous lineages of cancer cells that comprise the tumor” by an American Association for Cancer Research (AACR) workshop (Clarke et al. 2006). The first malignancy in which cells with stem-cell-like characteristics were detected was acute myeloid leukemia (Bonnet & Dick 1997). In a tumor model of nonobese diabetic/severe combined immunodeficiency (NOD/SCID) mice xenografted with human colon cancer cells (O’Brien et al. 2007), it was shown that there was only one cancer cell in 5.7×104 unfractionated tumor cells capable of tumor initiation. All of these cells were CD133 + but only one CD133+ cell in 262 was capable of tumor initiation. Xenografts generated from both tumor bulk and CD133+ colon cancer cells resembled the original patient tumor. This finding suggests that also CRC cells are hierarchically organized and a proportion of CD133+ cells in the tumor represent CRC stem cells. That would hold important implications for therapeutic strategies that could target the cancer-initiating cells (Kreso et al. 2014). However, it is still unclear whether there is interconversion between cells capable and incapable of tumor initiation, which would decrease the importance of targeting these cells with the treatments. Moreover, there is a need for more specific markers for CRC stem cells than CD133 in order to be able to further characterize their genetic properties, function, and clinical significance. 2.3.4 Invasion and metastasis The presence of CRC is histologically defined by the invasion through the muscularis mucosae into the submucosa (Hamilton et al. 2010). The patterns of tumor cell invasion can be classified into individual-cell migration, multicellular migration, and expansive growth without migration, which can be further divided into subcategories (Table 5, Fig. 3). The migration mechanisms of an individual cell are similar to those occurring in normal non-neoplastic cells in physiological conditions, including cell polarization and protrusion, adhesion formation, actinand myosin-based contraction, and rear detachment (Lauffenburger & Horwitz 1996, Ridley et al. 2003). Different patterns of invasion are guided by the expression of cell-matrix adhesion molecules (e.g., integrins), cell-cell adhesion molecules (e.g., cadherins), matrix-degrading enzymes (e.g., MMPs), and cell-cell communication molecules (e.g., chemokines) (Friedl et al. 2012). 28 Table 5. Patterns of cancer cell invasion. Pattern of invasion Designating features Individual-cell migration Tumor cells invading as single cells; absence of cell-cell adhesion (e.g., down-regulation of cadherin expression) Ameboid single-cell migration Low levels of cell-matrix adhesion (e.g., down-regulation of integrin expression) Mesenchymal single-cell migration Multicellular migration High levels of cell-matrix adhesion Tumor cells invading as cell strands, sheets, files or clusters Multicellular streaming Individual cells moving one after another using the same path within the tissue (e.g., guided by a chemotactic gradient) Collective cell migration Migration as a cohesive, multicellular group; high levels of cell-cell adhesion Expansive growth without migration Proliferating cell masses with intact cell-cell junctions, leading to outward pushing of surrounding tissue structures Classification and designating features adapted from Friedl & Alexander 2011, Friedl et al. 2012. Individual cell migration Ameboid Multicellular streaming Collective cell migration Expansive growth without migration Mesenchymal Fig. 3. Patterns of cancer cell invasion. Arrows indicate the direction of invasion. Modified from Friedl & Alexander 2011, Friedl et al. 2012. Each tumor frequently presents with multiple patterns of invasion (Friedl et al. 2012). About one in four CRCs shows infiltrative tumor border configuration, characterized by finger-like protrusions of the invasive front and representing collective cell migration as strands, while the rest show a rather expansive tumor border configuration (Jass et al. 1996). At high magnification, tumor buds — defined as isolated tumor cells or clusters of two to four cells at the invasive margin of the tumor — can be observed in the majority of CRCs (Hase et al. 29 1993, Ueno et al. 2002), and cytoplasmic pseudofragments — i.e., dendritic processes of the budding cells — are present in half of the patients with highgrade budding, (Shinto et al. 2005). Tumor budding is considered to represent weakening of cell-cell adhesions and it often includes individual cell migration (Natalwala et al. 2008). Accordingly, it has been associated with decreased expression of the cell adhesion molecule E-cadherin (Zlobec et al. 2007a). CRC commonly uses lymphatic vessels (Minsky et al. 1989) and blood vessels (Krasna et al. 1988) as routes of metastasis. The epithelial to mesenchymal transition (EMT) and single cell migration may enhance the efficacy of metastasis (Christiansen & Rajasekaran 2006). However, clusters of circulating tumor cells can be observed in CRC (Molnar et al. 2001) and other carcinomas including lung cancer (Hou et al. 2011), suggesting that collective vascular invasion may also take place. The phenotype of circulating tumor cells may influence the site of metastasis, as proposed by a human colon cancer xenograft mouse model that reported CD110+ cells being more likely to form liver metastases and CUB domain-containing protein 1 expressing cells being more likely to form lung metastases (Gao et al. 2013). Matrix metalloproteinases MMPs are a family of structurally related but genetically distinct zinc-dependent endoproteases participating in ECM degradation and thus facilitating tumor invasion (Stetler-Stevenson et al. 1993). MMP functions are regulated at the levels of gene expression, zymogen activation, interaction with ECM, and endogenous regulatory proteins, most notably TIMPs (Visse & Nagase 2003). CRC shows increased expression of several MMPs including MMP-1, -3, -7, -9, 10, -11, -12, and -14 (Asano et al. 2008), supporting their relevance in CRC pathogenesis. In addition to ECM degradation and tumor invasion, MMPs may also contribute to other functions in CRC, since in the past two decades, it has been established that some of the MMPs also participate in the regulation of growth signaling, apoptosis, angiogenesis, and immune responses (Egeblad & Werb 2002). Especially, MMP-8 has been shown to have an important role in controlling immune responses through its capability to cleave inflammatory mediators, including CXCL5, CXCL8, CXCL9, and CCL2 (Van Lint & Libert 2007). Knockout mice models have also associated MMP-8 with a protective role 30 against cancer (Balbin et al. 2003, Korpi et al. 2008). However, little is known of the function of MMP-8 in CRC. 2.3.5 Immune system and inflammation In the past decade, tumor-host interactions have increasingly been acknowledged as important players in cancer pathogenesis (Hanahan & Weinberg 2011). In addition to tumor cells, tumors comprise, e.g., fibroblasts, blood vessel endothelium, muscle cells, and immune cells (Tlsty & Coussens 2006). It has been established that the immune system can elicit an anti-tumor immune response (Koebel et al. 2007, Shankaran et al. 2001), and accordingly, numerous studies have associated increased immune cell infiltration in CRC with better disease outcome (Roxburgh & McMillan 2012). However, certain patterns of inflammation can also promote the development as well as the progression of cancer (Mantovani et al. 2008). The basics of the immune system function The immune system is an interacting network of organs, tissues, cells, and cell products that detects, repels, and eradicates pathogens and foreign molecules (Parkin & Cohen 2001). The immune system can be classified into the innate and adaptive immune systems, of which innate immunity mediates immediate, nonspecific immune responses by, e.g., granulocytes and macrophages, while adaptive immunity mediates antigen-specific immune responses by lymphocytes (Medzhitov & Janeway 1997). The functions associated with some of the major immune cell types are presented in Table 6. 31 Table 6. Functions associated with some of the major immune cell types. Cell type Functions References Lymphocytes T cells T helper cells Cell-mediated adaptive immunity Recruitment of other immune cells; activation Zhu & Paul 2010 of cytotoxic T cells and macrophages; help of maturation of B cells into plasma cells Cytotoxic T cells B cells Cytolytic destruction of target cells Barry & Bleackley 2002 Humoral, antibody-mediated immunity Pieper et al. 2013 Cytolytic destruction of target cells, Kolaczkowska & Kubes 2013 Granulocytes Neutrophils phagocytosis, and the secretion of proinflammatory mediators in the acute inflammatory responses Eosinophils Cytolytic destruction of target cells and the Rothenberg & Hogan 2006 secretion of proinflammatory mediators in parasitic infections and allergic reactions Basophils Secretion of proinflammatory mediators in Min 2008 allergic reactions, as well as in pathogen defense Macrophages Phagocytosis, antigen presentation, and the Murray & Wynn 2011 secretion of inflammatory mediators to recruit other immune cells Dendritic cells Antigen capture and presentation, T cell Banchereau et al. 2000 activation, and the secretion of inflammatory mediators to recruit other immune cells Mast cells Secretion of proinflammatory mediators in Abraham & St John 2010 allergic reactions as well as in pathogen defense The innate and adaptive immune systems are highly integrated, and the innate response generally precedes and is essential for the adaptive response (Medzhitov & Janeway 1997). Acute inflammation is usually the initial response to infectious agents and tissue injury (Medzhitov 2008), and it is characterized by tissue infiltration of neutrophils and other cells of innate immunity. If the acute inflammatory response fails to eradicate the pathogen or if the tissue damage is persistent, chronic inflammatory responses arise, characterized by increased tissue infiltration of macrophages and lymphocytes (Medzhitov 2008), as well as the generation of tertiary lymphoid tissue, i.e., lymphoid follicles, where several processes enhancing the adaptive immunity can take place, including T-cell 32 priming, clonal expansion, affinity maturation, and immunoglobulin class switching (Table 7) (Aloisi & Pujol-Borrell 2006). These processes also occur in the secondary lymphoid tissue, i.e., lymph nodes and spleen (Aloisi & PujolBorrell 2006, Pieper et al. 2013). To regulate the immune responses, the immune cells secrete cytokines that comprise a broad category of small proteins contributing to cell growth, differentiation, and activation (Commins et al. 2010). Table 7. Important events in the generation of adaptive immune responses. Event Definition T cell priming A process that takes place when a specific antigen is presented for the first time to a naïve T lymphocyte, causing it to differentiate into an effector cell (e.g., Th cell or cytotoxic T cell) or a memory cell Antigen presentation A process in which DCs, macrophages, and other immune cell types display the T cells an antigen to enable its recognition Clonal expansion A process in which primed lymphocytes proliferate and amplify their population B cell affinity maturation A process by which B cells produce antibodies with increased affinity for antigen through somatic hypermutation and clonal selection Somatic hypermutation A process where point mutations accumulate in the variable region genes of immunoglobulin heavy and light chains genes, leading to diversity in the antibody repertoire. Clonal selection A process in which only the B cells with the highest affinities for antigen are selected to survive. Immunoglobulin class switching A process in which a B cell changes its antibody production from one class to another, e.g., from IgM to IgG Definitions adapted from Aloisi & Pujol-Borrell 2006, Pieper et al. 2013. Especially, CD4+ helper T cells (Th cells) have critical roles in the adaptive immunity, contributing to the recruitment of other immune cell types, to the activation of cytotoxic T cells and macrophages, and to the maturation of B cells into plasma cells (Zhu & Paul 2010). They can be categorized according to their cytokine production as well as transcription factor expression. Understanding on the functional properties of Th cells has improved in the recent decade, leading to the discovery of new cell lineages (Zhu & Paul 2010). The best characterized four Th cell lineages are presented in Table 8, and other proposed lineages include Th3 cells (Weiner 2001), Th9 cells (Schlapbach et al. 2014), and follicular Th cells (Cannons et al. 2013). 33 + Table 8. Four best characterized subsets of CD4 Th cells. Th cell subset Characteristic transcription Cytokines critical for factor induction Cytokine products Th1 T-bet IL-12 + IFNγ IFNγ Th2 GATA3 IL-4 + IL-2, IL7, TSLP IL-4, IL-5, IL-10, IL-13 Th17 RORγt TGFβ + IL6, IL-21, IL-23 IL-17a, IL-17f, IL-21, IL-22 TReg FoxP3 TGFβ + IL2 TGFβ Definitions adapted from Zhu & Paul 2010. Immunosurveillance Already in the early 1900s, it was suggested that the immune system can suppress tumor development (Ehrlich 1909). However, contemporary methodology did not allow to experimentally test the suggestion and it took more than fifty years until the hypothesis of immunosurveillance was established, stating that immune cells can recognize and eliminate nascent transformed cells (Burnet 1970). Animal models were used in testing the hypothesis but no difference was found in spontaneous or Methyl cyanoacrylate (MCA) induced cancer development between athymic mice and controls (Rygaard & Povlsen 1974, Stutman 1974, 1979), implying that the immunosurveillance hypothesis was incorrect. However, it is now known that athymic mice produce low amounts of T cells (Ikehara et al. 1984), not being completely immunodeficient. The immunosurveillance hypothesis was again reinforced in consequence of the development of knockout mice models in the late 1990s and early 2000s, allowing the examination of the significance of individual genes in cancer. These models confirmed that several strains of mice lacking specific components of the immune system are susceptible to both chemically induced and spontaneous carcinogenesis. These components, concluded to contribute to cancer immunosurveillance, include interferon-γ (IFN-γ) and perforin that are important in the function of cytotoxic T cells (Street et al. 2001, 2002) and recombinationactivating gene 2 (RAG-2) that is essential for the generation of mature B and T lymphocytes (Shankaran et al. 2001). Also several pieces of evidence from human studies support the concept of immunosurveillance. First, the use of immunosuppressive drugs after organ transplantation has been shown to increase cancer risk (Pfeiffer et al. 2011). For example, Nordic kidney transplantation patients have a more than three times higher risk for CRC relative to the general population (Birkeland et al. 1995). 34 Second, intensive immune cell infiltration is associated with improved survival in CRC (Roxburgh & McMillan 2012), as well as several other solid tumors (Fridman et al. 2012). Third, circulating T cells specific for tumor-associated antigens (TAAs) have been found in patients with different solid tumors, including CRC (Nagorsen et al. 2000) and melanoma (Lee et al. 1999). Immunoediting The immunoediting hypothesis was established in the early 2000s to complement the limitations of the immunosurveillance hypothesis (Dunn et al. 2002). It acknowledges the Darwinian selection pressure produced by the immune system trying to eliminate the tumor cells. Moreover, the events leading to tumor progression are further elucidated. The foundation of the hypothesis was established with experiments with RAG-2 knockout mice (Shankaran et al. 2001). The researchers chemically induced sarcomas in the RAG2−/− mice and wild-type mice. The tumors from both RAG2−/− mice and wild type mice grew progressively when transplanted into RAG2−/− mice. Instead, wild-type mice rejected eight of twenty of the tumors derived form RAG2−/− mice but none of the tumors from wild-type mice. This finding indicated that the tumors originating in RAG2−/− mice were more immunogenic, suggesting that the immune system shapes the properties of the cancer cells. According to the immunoediting theory, the interactions between tumor and host can be classified into three phases: elimination, equilibrium, and escape (Schreiber et al. 2011). Elimination. It is thought that the immune system can eliminate most transformed cells before the development of a clinically detectable tumor (Schreiber et al. 2011). The mechanisms of immune system activation in cancer are under research. The malignant colorectal tumors contain about 80 amino acidaltering mutations that are absent in normal cells (Wood et al. 2007), potentially leading to the expression of TAAs, i.e., proteins that the immune system can recognize as altered. Moreover, as tumor cells proliferate, dying cells may release damage-associated molecular patterns (DAMPs) (Rakoff-Nahoum & Medzhitov 2009), potentially acting as danger signals, inducing the activation of immune system (Matzinger 1994). Equilibrium. Equilibrium describes the phase in which the immune system can restrict the tumor growth but, in the process, selects tumor cell variants with 35 increasing capabilities to survive from immune destruction. In addition to the earlier described hallmark study of Shankaran et al. (2001), the existence of an equilibrium phase has been more directly shown in a model of immunocompetent mice (Koebel et al. 2007). The mice were first injected with MCA. At 200 days after the injection, fifteen of nineteen tumor-free mice were treated weekly with anti-CD4/-CD8/-IFNγ antibodies. After this depletion of the immune system, tumors swiftly appeared at the site of the MCA injection in half of the mice, indicating that the immune system was maintaining occult cancer in an equilibrium state. Also clinical evidence supports the existence of an equilibrium state, since there are reports of organ transplantation patients developing cancer in the transplanted organ soon after the transplantation. For example, two kidney transplantation patients — having received kidneys from the same donor who had had melanoma 16 years previously but was thought to be cured — developed metastatic melanoma in one to two years after transplantation without evidence of primary cutaneous tumor (MacKie et al. 2003). Moreover, recurrences of cancer can be seen after years of asymptomatic phase (Stearns et al. 2007), and colorectal adenomas, when left unresected, may spontaneously regress in size (Hofstad et al. 1996). Escape. As a consequence of constant immune selection pressure, the tumor cells develop traits that help them to escape from immune control (Shankaran et al. 2001). Accordingly, advanced CRC shows lower numbers of tumor-infiltrating inflammatory cells (Klintrup et al. 2005). Moreover, the patients with metastatic tumors often present with increased immunological tolerance towards TAAs, the mechanisms of which may include ignorance (low antigen concentration or ineffective antigen presentation), anergy (lack of costimulatory signals to the lymphocytes), and active, cell-mediated immunosuppression by, e.g., regulatory T cells (Zou 2005). In CRC, the specific mechanisms of tumor escape from immune control are not well-defined but may include, e.g., the induction of T cell death through the release of proapoptotic microvesicles (Huber et al. 2005) and the downregulation of major histocompatibility complex (MHC) class I expression (Simpson et al. 2010). Tumor promoting inflammation Certain patterns of inflammation can promote the development as well as the progression of cancer (Mantovani et al. 2008). Convincing evidence links IBDs 36 — ulcerative colitis (Eaden et al. 2001) and Crohn’s disease (von Roon et al. 2007) — with increased CRC risk, and therefore, regular endoscopic surveillance is recommended for IBD patients (Itzkowitz & Present 2005). Conversely, the use of non-steroidal anti-inflammatory drugs (NSAIDs) decreases the CRC risk (Din et al. 2010). The distinctive difference between the inflammatory reaction observed in IBD and the anti-tumor immune response is the target of the immune attack. In cancer, the immune response is targeted towards the transformed cells, as indicated by, e.g., the presence of circulating T cells specific for TAAs (Lee et al. 1999, Nagorsen et al. 2000), while in IBDs it has been suggested to result from an inappropriate inflammatory response against intestinal microbes (Abraham & Cho 2009). With their potential to cause DNA mutations, reactive oxygen species (ROS) is considered a contributor to increased cancer risk related to inflammation (Wiseman & Halliwell 1996). Recent studies have also connected inflammation with increased proliferative signaling and angiogenesis, as well as the inhibition of apoptosis (Mantovani et al. 2008). More specifically, inflammatory cells are considered important sources of cytokines and chemokines (e.g., IL-1β), proteinases (e.g., MMPs), and growth factors (e.g., VEGF) that favor tumor growth (Hanahan & Coussens 2012). The signaling pathways associated with cancer-promoting inflammation function downstream of oncogenic mutations (Mantovani et al. 2008). One of the mediators of cancer-promoting inflammation is considered to be the activation of transcription factor nuclear factor-κB (NF-κB) signaling, downstream from, e.g., Toll-like receptor (TLR) – MyD88 pathway (RakoffNahoum & Medzhitov 2007) and the pathways of inflammatory cytokines TNF-α and IL-1β (Gilmore 2006). Utilizing conditional knockout mice, it has been established that inactivation of NF-κB signaling attenuates tumor formation in inflammation-associated hepatocellular cancer (Pikarsky et al. 2004) and colitisassociated cancer (Greten et al. 2004). These findings have been attributed to the ability of NF-κB to increase the synthesis of pro-survival and pro-proliferative molecules, proinflammatory cytokines, adhesion molecules, and ROS (BenNeriah & Karin 2011). Like NF-κB, signal transducer and activator of transcription 3 (STAT3) is a downstream mediator of numerous cytokines, as well as oncogenes (Yu et al. 2009). It has been shown to increase tumor cell survival, invasion, and proliferation (Yu et al. 2009), and mice with STAT3 conditional deletion in 37 enterocytes are less susceptible to tumors in a colitis-associated cancer model (Grivennikov et al. 2009). To summarize the potential role of inflammation in cancer initiation and progression, it has been hypothesized that cancer and inflammation are connected by two pathways, intrinsic and extrinsic (Mantovani et al. 2008). The intrinsic pathway is activated by oncogenic changes in neoplasia and the extrinsic pathway stems from factors not related to cancer (e.g., pathogens, IBD). Both pathways lead to the increased production of inflammatory mediators with potential capabilities to promote the development and progression of cancer such as proinflammatory cytokines through NF-κB, and STAT3 (Mantovani et al. 2008). However, this model does not acknowledge tumor immunosurveillance. Therefore, this review adopts a modified version of the original model (Fig. 4), with an additional TAA-driven intrinsic pathway leading to an anti-tumor immune response, but also accompanied by potential capabilities to promote the development and progression of cancer through, e.g., NF-κB, and STAT3. 1. Oncogen-driven intrinsic pathway 3. Extrinsic pathway Oncogen activation Transcription factor (NF-κB, STAT3) activation in tumor cells Inflammation or infection Chemokine and cytokine production by tumor cells 2. TAA-driven intrinsic pathway Genetic alterations Immune response against TAAs Generation of TAAs Cancer immunosurveillance Inflammatory cell recruitment Chemokine and cytokine production by inflammatory cells, tumor cells and stromal cells Transcription factor (NF-κB, STAT3) activation in tumor cells, inflammatory cells, and stromal cells Tumor-promoting inflammation - Cell proliferation and survival - Angiogenesis - Tumor cell invasion - Inhibition of apoptosis Fig. 4. Pathways connecting inflammation and cancer. Modified from Mantovani et al. 2008. 38 2.3.6 Angiogenesis Tumors require blood vessels in order to acquire nutrients and oxygen (Hanahan & Weinberg 2011). The signaling molecule vascular endothelial growth factor (VEGF) harbors a central role in angiogenesis regulation (Carmeliet 2005) and is frequently overexpressed in CRC (Ishigami et al. 1998). In CRC, high VEFG expression (Ishigami et al. 1998, Lee et al. 2000) and high blood vessel density (Tanigawa et al. 1997) have been associated with worse prognosis, indicating that the tumors with effective angiogenesis show aggressive behavior. In addition to tumor cells, the inflammatory cells in tumor stroma are important sources of angiogenesis regulatory molecules (Zumsteg & Christofori 2009), and breast cancer mouse models have indicated that tumor-associated macrophages (TAMs), especially, are important in pressing the angiogenic switch (Lin et al. 2006). 2.4 Colorectal cancer diagnosis and screening CRC patients frequently present to primary care with abdominal symptoms, including rectal bleeding, diarrhea, loss of weight, abdominal pain, and anemia (Jellema et al. 2010). The diagnosis of CRC is most often made by colonoscopy, an endoscopic procedure that enables visualization of the entire mucosa of the colon and rectum (Hazewinkel & Dekker 2011). Different methods for CRC screening have been suggested, including fecal occult-blood screening (Mandel et al. 2000), sigmoidoscopy (Schoen et al. 2012), and computed tomographic colonography (Pickhardt et al. 2007). No serum markers for screening have been validated for clinical work (Sturgeon et al. 2008). In Finland, an organized CRC screening program with fecal occult blood test as a public health policy was started in 2004 and it covered one third of the target population by 2007 (Malila et al. 2011). The test sensitivity — the proportion of the diseases the test is able to identify in those screened — in Finland in 20042006 was 54.6% (Malila et al. 2008). An invasive colorectal cancer was diagnosed in 8.2% of the fecal occult blood positive subjects at screening or during follow-up in 2004-2006 and the testing was considered cost-effective (Paimela et al. 2010). 39 2.5 Colorectal cancer prognostic and predictive markers Despite the overall improvement in CRC survival in the past decades, understanding of why individual patients get a recurrence while others do not remains poor (Walther et al. 2009). Prognostic markers provide information about the patients’ cancer outcome, while predictive markers indicate sensitivity or resistance to a specific therapy (Oldenhuis et al. 2008). The current standard for CRC prognostication is clinicopathological staging, while potential molecular prognostic and predictive markers have attracted vast interest in the past decades (Walther et al. 2009). Several parameters can be used in the measurement of survival time (Punt et al. 2007). Overall survival (OS) — time from diagnosis to death, irrespective of cause — is frequently used, when the cause of death is not specified, while causespecific survival (or cancer-specific survival CSS) — time to death caused by the same cancer — can be calculated if the cause of death information is available (Heinävaara et al. 2002, Punt et al. 2007). In the past decades, the use of diseasefree survival (DFS) — the period after curative treatment when no disease can be detected — has become more frequent in the clinical trials as well as prognostic studies, offering an earlier accomplishment of sufficient numbers of endpoints relative to OS and CSS (Abrams 2005, Birgisson et al. 2011). 2.5.1 Clinical and histopathological prognostic factors TNM Stage Colorectal cancer staging is based on TNM classification (Sobin et al. 2009), T denoting primary tumor, N regional lymph node metastasis, and M distant metastasis. It is more accurate than Dukes’ classification (Dukes 1932) and its modifications (Turnbull et al. 1967), which were used earlier for many decades. The stages A-D in the Turnbull (1967) modification roughly correspond to stages I-IV in TNM6 and TNM7 (Sobin et al. 2009). In patients operated on in the 1990s, the OS ranged from 90% in stage I to less than 10% in stage IV (O’Connell et al. 2004). In addition to guiding the prognostic classification, tumor stage is the most important factor directing the treatment of the patients (Quirke et al. 2007). The changes from TNM6 to TNM7 have been relatively modest, so that the main T, N, and M categories and stages I, II, III, and IV can be compared between 40 TNM6 and TNM7 (Table 9, Table 10). An important change is the categorization of tumor deposits in the pericolorectal adipose tissue without histological evidence of residual lymph node, which are classified in the N category as N1c in TNM7 but counted as lymph nodes in TNM6 if harboring a smooth contour, or classified in T category and blood vessel invasion if harboring an irregular contour (Nagtegaal et al. 2012, Sobin et al. 2009, Sobin & Wittekind 2002). Table 9. TNM6 and TNM7 Classification. Classification TNM6 TNM7 TX Primary tumor cannot be assessed Primary tumor cannot be assessed T0 No evidence of primary tumor No evidence of primary tumor Tis Carcinoma in situ: intraepithelial or Carcinoma in situ: intraepithelial or invasion of lamina propria invasion of lamina propria T1 Tumor invades submucosa Tumor invades submucosa T2 Tumor invades muscularis propria Tumor invades muscularis propria T3 Tumor invades through the Tumor invades through the muscularis muscularis propria into subserosa propria into subserosa or into non- or into non-peritonealized pericolic peritonealized pericolic or perirectal or perirectal tissues tissues Primary tumor (T) T4 Tumor directly invades other organs Tumor directly invades other organs or or structures and/or perforates structures and/or perforates visceral visceral peritoneum peritoneum T4a - Tumor perforates visceral peritoneum T4b - Tumor directly invades other organs or structures Regional lymph nodes (N) NX Regional lymph nodes cannot be Regional lymph nodes cannot be assessed assessed N0 No regional lymph node metastasis No regional lymph node metastasis N1 Metastasis in 1–3 regional lymph Metastasis in 1–3 regional lymph nodes nodes N1a - Metastasis in one regional lymph node N1b - Metastasis in 2–3 regional lymph nodes N1c - Tumor deposit(s) in the subserosa, or in the nonperitonealized pericolic or perirectal tissues without regional lymph node metastasis N2 N2a Metastasis in 4 or more regional Metastasis in 4 or more regional lymph lymph nodes nodes - Metastasis in 4–6 regional lymph nodes 41 Classification N2b TNM6 TNM7 - Metastasis in 7 or more regional lymph nodes Distant metastasis (M) MX Distant metastasis cannot be Distant metastasis cannot be assessed assessed M0 No distant metastasis No distant metastasis M1 Distant metastasis Distant metastasis - Metastasis confined to one organ or M1a lymph node metastasis outside regional lymph nodes M1b - Metastasis in more than one organ or the peritoneum Adapted from Sobin & Wittekind 2002, Sobin et al. 2009. Table 10. Stage Classification. Stage Dukes’ (Turnbull Definition in TNM6 Definition in TNM7 modification) 0 Tis, N0, M0 Tis, N0, M0 I A T1–2, N0, M0 T1–2, N0, M0 II B T3–4, N0, M0 T3–4, N0, M0 IIA B T3, N0, M0 T3, N0, M0 IIB B T4, N0, M0 T4a, N0, M0 IIC B - T4b, N0, M0 C T1–4, N1–2, M0 T1–4, N1–2, M0 IIIA C T1–2, N1, M0 T1–2, N1, M0 or T1, N2a, M0 IIIB C T3–4, N1, M0 T3–4a, N1, M0 or T2–3, N2a, IIIC C T1–4, N2, M0 D T1–4, N0-2, M1 T1–4, N0-2, M1 IVA D - T1–4, N0-2, M1a IVB D - T1–4, N0-2, M1b III M0 or T1-2, N2b, M0 T4a, N2a, M0 or T3–4a, N2b, M0 or T4b, N1–2, M0 IV Adapted from Sobin & Wittekind 2002, Sobin et al. 2009, Turnbull et al. 1967. Lymph node examination The number of lymph nodes examined is dependent on the number of lymph nodes present in the tissue, the surgical technique, and the examination of the tissue by the pathologist. A low number of examined nodes has been associated 42 with poor survival in several independent large cohorts (Le Voyer et al. 2003, Sarli et al. 2005, Stocchi et al. 2011, Swanson et al. 2003). This may be related to the quality of the examination of the surgical specimens by a pathologist. Accurate staging associates with correct choices of therapies. Moreover, it has been hypothesized that a decreased number of examined lymph nodes may reflect a diminished immune response (Sarli et al. 2005). Preoperative radiotherapy (Nagtegaal et al. 2002) and chemoradiotherapy (Morcos et al. 2010) in rectal cancer result in a decrease in lymph node yield. However, it has been reported that the number of lymph nodes in these specimens does not influence survival (Rullier et al. 2008). Residual tumor and resection margins The prognosis of CRC is influenced by the completeness of tumor removal at the time of surgery (Hamilton et al. 2010, Sobin et al. 2009), which can be evaluated utilizing the residual tumor (R) classification as presented in Table 11. In rectal cancer, the circumferential (radial) resection margin adjacent to the deepest point of tumor invasion has been found to be a powerful predictor of survival and the development of distal metastasis (Nagtegaal & Quirke 2008). Short longitudinal (proximal and distal) resection margins (less than 2–5 cm, depending on the location of the tumor and the type of the resection) are also considered to associate with adverse prognosis in colon cancer and, especially, in rectal cancer (Bernstein et al. 2012, Nelson et al. 2001). Table 11. Residual tumor classification. Classification Definition RX The presence of residual tumor cannot be assessed R0 No residual tumor; all margins histologically negative R1 Incomplete tumor resection with microscopic surgical margin involvement R2 Incomplete tumor resection with macroscopic residual tumor Adapted from Sobin et al. 2009, Wittekind et al. 2002. Bowel obstruction and perforation Emergency operative interventions in CRC are required in cases of bowel obstruction and perforation, which have been associated with poor survival and, especially, with increased perioperative mortality (Chen & Sheen-Chen 2000). 43 Grade of differentiation The current WHO classification categorizes colorectal adenocarcinomas into well, moderately, and poorly differentiated adenocarcinomas, and undifferentiated carcinomas based on the percentage of gland formation (Table 12), but also various other grading criteria have been suggested (Compton et al. 2000). Specific types of CRC, such as signet ring cell carcinomas, are not graded because these tumors in general behave like high grade tumors. High grade (3–4) has been associated with worse prognosis in several large cohorts independent of tumor stage (Chapuis et al. 1985, Halvorsen & Seim 1988, Newland et al. 1994). Table 12. Grade classification. Grade Category Definition 1 Well-differentiated >95% with gland formation 2 Moderately differentiated 50–95% with gland formation 3 Poorly differentiated <50% with gland formation 4 Undifferentiated No evidence of differentiation Adapted from Hamilton et al. 2010. Blood vessel invasion, lymph vessel invasion, and perineural invasion Blood vessel invasion (Chapuis et al. 1985, Newland et al. 1994, Roxburgh et al. 2010), lymphatic vessel invasion (Akagi et al. 2013, Minsky et al. 1989), and perineural invasion (Liebig et al. 2009) have been reported to be stageindependent markers for worse prognosis in CRC. In some cases, their observation can be difficult utilizing sections stained with hematoxylin and eosin (H&E), and histochemical and immunohistochemical methods (Table 13) may improve the accuracy of their assessment (Kojima et al. 2013, van Wyk et al. 2013). Table 13. Histochemical and immunohistochemical methods for the detection of lymph vessel invasion and blood vessel invasion. Method References Lymphatic invasion Podoplanin (D2-40) Ishii et al. 2009, Liang et al. 2007, Matsumoto et al. 2007, Suzuki et al. 2009 Blood vessel invasion Elastica 44 Roxburgh et al. 2010, Akifumi Suzuki et al. 2009 Tumor border configuration Infiltrative tumor border, characterized by diffuse, irregular tumor borders and finger-like processes of tumor cells invading the surrounding stroma (Fig. 5), is present in about 25% of CRC (Jass et al. 1996). It is associated with adverse prognosis in CRC, independent of tumor stage (Jass et al. 1987, Morikawa et al. 2012, Zlobec et al. 2009). It can be best evaluated with low magnification (Koelzer & Lugli 2014). Using a high magnification, tumor budding (Fig. 5), composed of isolated tumor cells or clusters of two to five cells at the invasive margin of the tumor, can be observed in the majority of CRC (Hase et al. 1993, Ueno et al. 2002). Intensive budding is associated with a poor prognosis independent of tumor stage (Hase et al. 1993, Hörkkö et al. 2006, Ueno et al. 2002, Zlobec et al. 2011). a b c d Fig. 5. Representative images of tumor border configuration and tumor budding. a) Infiltrative growth pattern characterized by poorly demarcated tumor borders and streaming dissection of muscularis propria. (b) A well-demarcated pushing tumor border. (c) Intensive tumor budding defined as individual cells or clusters of up to five cells. (d) No tumor budding can be observed. 45 2.5.2 Inflammation-based prognostic markers This issue is reviewed in detail, because it is one of the main topics of the thesis. Colorectal tumors are infiltrated by a heterogeneous group of immune cells in various tumor locations (Fig. 6). Several general inflammatory classifications as well as specific inflammatory cell markers have been associated with stageindependent prognostic value in CRC (Roxburgh & McMillan 2012). CT-IEL CT-S CT-IEL CT IM CLR Fig. 6. Colorectal cancer associated immune cell infiltrate in different tumor locations. The locations can be divided into intratumoral (CT: center of tumor) and peritumoral (IM: invasive margin). Intratumorally, intraepithelial (IEL) and stromal (S) locations can be distinguished. Colorectal cancer associated lymphoid reaction (CLR) is defined as transmural lymphoid aggregates surrounding the tumor. General inflammatory classifications Dense peritumoral inflammatory reaction is associated with better survival of the CRC patients independent of tumor stage (Halvorsen & Seim 1989, Jass 1986, 46 Jass et al. 1987, Klintrup et al. 2005, Ogino et al. 2009, Roxburgh et al. 2009). Different methods utilized in its evaluation from H&E slides are portrayed in Table 14. Table 14. Some of the most frequently applied methods for the evaluation of peritumoral inflammatory cell infiltration in colorectal cancer. References Method Jass 1986, Jass et al. 1987, 1996 Two-tiered classification into low- and high-grade according to the presence of distinctive connective tissue mantle at the invasive margin scattered with lymphocytes and other inflammatory cells Halvorsen & Seim 1989 Two-tiered classification of the amount of inflammatory cells along the entire tumor edge away from areas of frank abscess formation, classified into prominent and inconspicuous Klintrup et al. 2005 Two-tiered classification of peritumoral inflammatory infiltrate, where low-grade denotes mild or patchy immune cell infiltrate and high-grade denotes a band-like immune cell infiltrate with evidence of the destruction of cancer cell islets Ogino et al. 2009 Four-tiered scoring of the discrete lymphoid reactions surrounding tumor into 0 (absent), 1+ (mild), 2+ (moderate), or 3+ (marked) Crohn’s-like lymphoid reaction A subset of CRCs exhibits CLR, an inflammatory reaction pattern comprising transmural lymphoid aggregates (Fig. 6) (Graham & Appelman 1990). It has been associated with better survival in CRC (Adams & Morris 1997, Buckowitz et al. 2005, Harrison et al. 1994, Murphy et al. 2000, Ogino et al. 2009). However, the stage-independent prognostic value of CLR, as well as its biological mechanisms and cellular composition have not been well-defined. T cells High infiltration of CD3+, CD8+, and CD45RO+ T cells in different tumor locations has almost consistently been associated with better survival in CRC and has been linked with stage-independent prognostic value, although not replicated in all of the studies (Table 15). Immunoscore (Galon et al. 2012, 2014), composed of computer-assisted combined evaluation of two of the three markers (CD3, CD8, CD45RO) in two regions (CT, IM) has been reported to have superior prognostic significance relative to TNM classification (Mlecnik et al. 2011, Pagès 47 et al. 2009). Consequently, there is an international initiative to include Immunoscore in cancer classification (Galon et al. 2012, 2014). + + + Table 15. Prognostic significance of CD3 and CD8 , and CD45RO T cells. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Naito et al. 1998 CD8 131 I–IV CT-IEL No No 4-tiered + Yes Guidoboni et al. CD3, 109 II–III CT-IEL No No Yes + Yes 2001 CD8 Nagtegaal et al. CD3, 160 I–III CT-S, No No 3-tiered + No 2001 CD8 Chiba et al. 2004 CD8 Pagès et al. IM 371 I–IV CT-IEL No No Yes CD45RO 415 I–IV CT, IM Yes Yes Yes + + + Yes Yes I–IV CT, IM Yes Yes Yes + + Yes CT, IM Yes Yes Yes + + Yes, IM Yes No Yes + Yes + No 2005 Galon et al. 2006 CD3 415 Pagès et al. CD8, 411, I–II 2009 CD45RO 212 Lugli et al. 2009 CD8 combined 279, I–III 191 Salama et al. CD8, 2009 CD45RO Deschoolmeeste CD3, r et al. 2010 CD8 Nosho et al. CD3, 2010 CD8, 967 II–III CT Yes Yes Yes 215 I–IV CT, IM No No 4-tiered + + No 768 I–IV CT-IEL Yes Yes Yes + + Only CD45RO CD45RO Peng et al. 2010 CD3, 68 IIIb CT-S No No Yes + No CD3 462 I–IV CT-IEL Yes No Yes + Yes CD3 484 I–III CT-S, No No 4-tiered + Yes Yes Yes Yes CD45RO Simpson et al. 2010 Dahlin et al. 2011 CT-IEL, IM Mlecnik et al. CD3, 415, 2011 CD8, 184 CT, IM + + + Yes, combined CD45RO Richards et al. CD3, 2014 CD8, 365 I–III CT-S, CT-IEL, No No 4-tiered + Yes, CD3 (IM), CD8 CD45RO IM (CT-IEL) Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IEL: intraepithelial; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray. 48 FoxP3+ regulatory T (TReg) cells are immunosuppressive cells essential for maintaining peripheral tolerance. It was reported that the recruitment of TReg cells in ovarian carcinoma suppressed tumor-specific T cell immunity and predicted reduced survival (Curiel et al. 2004), and FoxP3+ cell infiltration has also been associated with adverse prognosis in breast cancer (Bates et al. 2006). However, in CRC, most studies have reported an association between higher FoxP3 + cell infiltration and better prognosis (Table 16). The mechanisms underlying this inconsistency between different types of solid tumors are not clear. Table 16. Prognostic significance of Regulatory T cells. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Salama et al. FoxP3 967 II–III CT Yes Yes Yes + Sinicrope et al. FoxP3/ 160 II–III CT-IEL No No Yes 2009 CD3 ratio Frey et al. 2010 FoxP3 1420 I–IV CT Yes No Yes Nosho et al. FoxP3 768 I–IV CT-IEL Yes Yes Yes + FoxP3 87 II CT-S, No Yes Yes 0 Yes 2009 - Yes + Yes + No 2010 Lee et al. 2010 + Yes, CT-IEL + Not CT-IEL Tosolini et al. FoxP3 415 I–IV CT, IM Yes Yes Yes Yoon et al. 2012 FoxP3 216 II–III CT-S, Yes No Yes No No 4-tiered 2011 estimated + Yes CT-IEL Richards et al. FoxP3 2014 365 I–III CT-S, + No CT-IEL, IM Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IEL: intraepithelial; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray. Natural killer cells A few studies have assessed the prognostic value of natural killer cells in CRC, utilizing CD56 and CD57 as markers in their identification, and have suggested that higher natural killer cell density may associate with improved survival (Table 17). 49 Table 17. Prognostic significance of natural killer cells. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Coca et al. 1997 CD57 157 I–III CT No No 3-tiered + + Yes Nagtegaal et al. 160 I–III CT-S, No No 3-tiered 0 + No No No Yes + No CD56 2001 IM Menon et al. CD56, 2004 CD57 93 II–III CT-S, CT-IEL, IM Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IEL: intraepithelial; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray. Dendritic cells A variety of DC markers exist. CD1a is generally expressed on immature DCs, while CD83 has been found to be stably expressed on activated, mature DCs (Cao et al. 2005). Dendritic cells also express S-100 protein that is present in cells derived from the neural crest (Zimmer et al. 1995). It has been reported that mature DCs make clusters with T cells in the invasive margin of CRC to promote T cell activation (Suzuki et al. 2002). However, few studies have addressed the prognostic significance of different dendritic cell populations in CRC (Table 18). Table 18. Prognostic significance of dendritic cells. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Dadabayev et al. S-100 104 II–III 2004 CT-S, No No Yes 0 CT-IEL, Not evaluated IM Sandel et al. S-100, 2005 CD1a 104 II–III CT-S, No No Yes + CT-IEL, Not evaluated IM Nagorsen et al. 2007 S-100 40 I–IV CT-S, CT-IEL No No Yes + Not evaluated Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IEL: intraepithelial; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray 50 Macrophages High macrophage infiltration at the IM of the tumors has been linked with favorable prognosis in CRC in several studies (Table 19). In addition to contributing to tumor cell phagocytosis, macrophages may control the immune reactions by the secretion of cytokines and growth factors (Mantovani et al. 2002). The alternatively activated M2 macrophages (characterized by, e.g., CD163 expression and IL-10 production) have anti-inflammatory activity and have been hypothesized to promote tumor growth, while the classically activated M1 macrophages (characterized by, e.g., inducible nitric oxide synthase, iNOS, expression and IL-12 production) have been thought to contribute to anti-tumor immune responses (Mantovani et al. 2002). However, Edin et al. (2012) recently reported that CD163- and iNOS-expressing macrophages harbor similar prognostic value. Instead, Ålgars et al. (2012) utilized Stabilin-1 as a marker for M2 macrophages and found that their abundance associated with poor prognosis irrespective of CD68+ macrophages. Table 19. Prognostic significance of macrophages. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Nagtegaal et al. CD68 160 I–III 2001 Lackner et al. CT-S, No No 3-tiered + + No IM CD68 70 II–III CT, IM No No Yes + Yes, IM CD68 117 I–IV CT-S, No No 4-tiered 0 No 2004 Baeten et al. 2006 CT-IEL, IM Forssell et al. CD68 488 I–IV IM No No 4-tiered CD163 40 I–IV CT-S, No No Yes + Yes 2007 Nagorsen et al. 2007 Ålgars et al. + Not CT-IEL evaluated Stabilin-1 159 II–IV CT, IM No No 4-tiered - Yes CD163, I–IV IM No No 4-tiered + No 2012 Edin et al. 2012 485 iNOS Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IEL: intraepithelial; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray. 51 Neutrophils In histological specimens, neutrophils can be detected with several different markers, including neutrophil elastase, CD66b, and myeloperoxidase (MPO), all of which have been found to have high sensitivity and specificity for neutrophils, although neutrophil elastase and MPO label a population of monocytes, basophils and eosinophils with a lower intensity (Paulsen et al. 2013, Pulford et al. 1988). The prognostic significance of neutrophil infiltration in CRC is controversial (Table 20). Table 20. Prognostic significance of neutrophils. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Nielsen et al. H&E 584 I–IV 1999 Nagtegaal et al. Sub- No No Yes + No No 3-tiered 0 No mucosa Elastase 160 I–III 2001 CT-S, + No IM Rao et al. 2012 CD66b 229 I–IV CT Yes No Yes - Yes Droeser et al. MPO 1491 I–IV CT Yes No Yes + Yes 2013 Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray. Eosinophils A few studies have addressed the prognostic significance of eosinophils in CRC and have indicated that their abundance may indicate better survival (Table 21). 52 Table 21. Prognostic significance of eosinophils. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Pretlow et al. Giemsa 70 II–III CT No No Yes + Not Fisher et al. 1989 H&E 331 I–III CT No No Yes + No Nielsen et al. 584 I–IV Sub- No No Yes + Yes 1983 evaluated H&E 1999 Fernández- mucosa H&E 126 I–III CT No No 4-tiered + + Yes EG-2 160 I–III CT-S, No No 3-tiered + + Only IM Aceñero et al. 2000 Nagtegaal et al. 2001 IM Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray. Mast cells In cancer, mast cells have been attributed both protective and promoting roles, involving controlling of anti-tumor immune response and contributing to angiogenesis (Khazaie et al. 2011). The studies evaluating the prognostic significance of mast cells in CRC have been controversial (Table 22). 53 Table 22. Prognostic significance of mast cells. Reference Marker N Stage Location TMA Comp Continuous OS CSS DFS Independent variables prognostic value Pretlow et al. Giemsa 70 II–III CT No No Yes 0 Not Fisher et al. 1989 H&E 331 I–III CT No No Yes - Yes Nielsen et al. 584 I–IV Sub- No No Yes + Yes No No 3-tiered + 1983 evaluated H&E 1999 Nagtegaal et al. mucosa Tryptase 160 I–III 2001 Acikalin et al. CT-S, + Only IM - No IM Giemsa 60 I–IV CT No No Yes Xia et al. 2011 Tryptase 93 IIIB CT-S No No Yes 0 Wu et al. 2013 Tryptase 325 I–III CT-S No No Yes - 2005 No - Yes Abbreviations: Comp: computer; CSS: cancer-specific survival; CT: tumor center; DFS: disease-free survival; IM: invasive margin; OS: overall survival; S: stromal; TMA: tissue microarray. 2.5.3 Genetic prognostic and predictive markers The prognostic significance of the major genetic changes associated with CRC pathogenesis (Fearon & Vogelstein 1990, Jass 2007a) has been under extensive research during the past decades. However, with the exception of activating mutation in RAS oncogene as a factor influencing epidermal growth factor receptor (EGFR) treatment outcome (Amado et al. 2008, Douillard et al. 2013, Karapetis et al. 2008, Lièvre et al. 2006), other markers are currently rarely employed in clinical decision-making. Microsatellite instability and chromosomal instability Numerous studies have evaluated the prognostic value of MSI and CIN. The results have been gathered as large meta-analyses, which indicate that MSI associates with better prognosis (hazard ratio, HR for OS 0.65, 95% confidence interval, CI 0.59-0.71) and CIN with worse prognosis (HR for OS 1.45, 95% CI 1.35-1.55) (Popat et al. 2005, Walther et al. 2008). MSI induces the generation of novel tumor-specific frameshift peptides, potentially increasing the immunogenicity (Schwitalle et al. 2008), and it has been hypothesized that the enhanced immune response against the tumors with 54 MSI is one of the contributing factors to the prognostic effect of MSI (Smyrk et al. 2001). MSI has also been reported to predict poor response to 5-fluorouracil-based chemotherapy in several large cohorts (Ribic et al. 2003, Sargent et al. 2010). On the contrary, an analysis of 2,141 CRC adjuvant trial patients proposed that stage III CRC patients with MSI benefit from 5-fluorouracil-based adjuvant treatment (Sinicrope et al. 2011). In conclusion, although the data on the predictive value of MMR status on 5-fluorouracil based chemotherapy is inconclusive, there is evidence supporting the validity of MSI in identifying a group of stage II CRC patients with a low likelihood of recurrence and who are therefore unlikely to benefit from chemotherapy (Schmoll et al. 2012). KRAS, NRAS and BRAF The prognostic significance of KRAS mutation status is controversial with some studies associating it with adverse prognosis (Eklöf et al. 2013, Richman et al. 2009) and others with little prognostic value (Fariña-Sarasqueta et al. 2010, Popovici et al. 2013, Roth et al. 2010). A recent meta-analysis that included 23 studies indicated that KRAS mutations overall do not correlate with CRC prognosis (HR for OS 1.04, 95% CI 0.99–1.10) (Ren et al. 2012). KRAS and NRAS have predictive value in CRC, since patients with RASmutated tumors do not benefit from EGFR antibody therapy (Amado et al. 2008, Douillard et al. 2013, Karapetis et al. 2008, Lièvre et al. 2006). RAS gene family members are downstream mediators of EGFR in the MAPK-ERK pathway (Roberts & Der 2007). BRAFV600E mutation has been reported to associate with poor prognosis in stage II–III (Fariña-Sarasqueta et al. 2010, Popovici et al. 2013, Roth et al. 2010), as well as stage IV CRC (Richman et al. 2009), although conflicting reports also exist (Hutchins et al. 2011). A recent meta-analysis provided BRAF mutation a HR for OS of 2.24 (95% CI 1.82–2.83) for 26 studies that met the inclusion criteria (Safaee Ardekani et al. 2012). An activating mutation in BRAF, located downstream RAS in the MAPK-ERK pathway, has also been linked with resistance to EGFR antibody treatment (Di Nicolantonio et al. 2008). 55 EGFR gene copy number An increase in EGFR gene copy number, as assessed by fluorescence and silver in situ hybridization, has been linked with response to anti-EGFR treatment (Sartore-Bianchi et al. 2007, Ålgars et al. 2011). However, the results of in situ hybridization can be hard to interpret, and a standardized and validated scoring system is needed before the test can be utilized in clinical practice (SartoreBianchi et al. 2012, Ålgars et al. 2014). Loss of heterozygosity in chromosome 18q A meta-analysis of 17 studies (Popat & Houlston 2005) suggested that CRCs with chromosome 18q loss have poorer prognosis. There was, however, evidence of substantial publication bias. Therefore, further large-scale studies have been conducted, and they have indicated that 18q LOH does not associate with patient survival (Bertagnolli et al. 2011, Ogino et al. 2009, Popat et al. 2007). Moreover, the potential value of 18q LOH as an independent prognostic marker is questionable, since it is a marker of CIN (Ogino et al. 2007), which is itself associated with worse prognosis (Walther et al. 2008). TP53 A meta-analysis of 61 studies (Munro et al. 2005) reported mutations in TP53 to associate with worse prognosis in CRC (relative risk 1.31, 95% CI 1.19–1.45). However, there was evidence of both publication bias and heterogeneity of results, which made it difficult to draw firm conclusions. A sequencing-based prospective study on 3,583 stage I–IV CRC patients indicated that TP53 mutation itself does not carry prognostic significance (Russo et al. 2005), while specific mutations in specific tumor sites may be associated with the outcome. 2.5.4 Blood and serum prognostic markers Tumor progression and metastasis is also reflected at the systemic level (McAllister & Weinberg 2014). Indeed, a number of systemic protein markers and hematological parameters have been associated with prognostic significance in CRC (Duffy et al. 2007, Sturgeon et al. 2008). Carcinoembryonic antigen (CEA) 56 is the most widely used marker in clinical work, and its higher preoperative concentrations have been found to associate with worse survival (Duffy 2001). Systemic inflammatory markers have also shown promise in CRC prognostication (Roxburgh & McMillan 2010). Especially, the Glasgow prognostic score (GPS) (McMillan 2013) — comprised of serum levels of Creactive protein (CRP) and albumin — and blood neutrophil/lymphocyte ratio (Ding et al. 2010, Walsh et al. 2005) have been found to have strong prognostic value in several independent cohorts. However, more validation is still needed, and GPS or neutrophil/lymphocyte ratio are not currently frequently utilized in clinical work (Duffy et al. 2007, Sturgeon et al. 2008). Proteolytic activity that is induced in tumor microenvironment is reflected by increased systemic levels of several MMPs in CRC, including MMP-7 (Maurel et al. 2007) and MMP-9 (Hurst et al. 2007, Mroczko et al. 2010). Furthermore, systemic levels of some MMPs, including MMP-1 (Tahara et al. 2010) and MMP7 (Maurel et al. 2007), have been reported to have prognostic significance. However, more studies are needed to determine their potential value in clinical practice. 2.6 Colorectal cancer treatment Surgery is the primary modality of treatment for CRC, and resection is the only therapy required for early-stage CRC (Nelson et al. 2001). Adjuvant chemotherapy, radiotherapy (RT), or chemoradiotherapy (CRT) reduces the mortality in surgically treated patients with a high risk of recurrence (André et al. 2004), and neoadjuvant RT or CRT improves the results of the treatment of locally advanced rectal cancer (Aklilu & Eng 2011, Onaitis et al. 2001). In addition to the traditional cytotoxic drugs, the last two decades have led to the introduction of monoclonal antibodies in CRC treatment (Cunningham et al. 2004, Hurwitz et al. 2004). 2.6.1 Surgical treatment The standard operative treatments for CRC are portrayed in Table 23. The target is to remove the tumor along with the associated lymphatics en bloc with 5–10 cm of normal bowel on either side of the primary tumor (Nelson et al. 2001). Usually, the remaining parts of the bowel are anastomosed together. However, in the abdominoperineal resection of the rectum, also the anus is removed and the end of 57 the remaining sigmoid colon is brought to the surface of the abdomen as a colostomy (Nagtegaal et al. 2005). Table 23. Standard surgical treatments of colorectal cancer. Operation Indications Characteristics Reference Right hemicolectomy Tumor in caecum or in, Resection of the proximal Lezoche et al. 2002 ascending or transverse colon colon Left hemicolectomy Tumor in descending or Resection of the distal colon Lezoche et al. 2002 transverse colon Transverse colectomy Tumor in transverse colon Resection of the transverse Schlachta et al. 2007 colon Sigmoid resection Tumor in sigmoid colon Resection of the sigmoid Fowler & White 1991 colon Anterior resection of Tumor in proximal or mid- Resection of rectum and the rectum, total rectum lymphovascular fatty tissue mesorectal excision Enker et al. 1995 surrounding the rectum with the preservation of anal sphincter Abdominoperineal resection of the rectum Tumor in distal rectum Resection of the anus and the Nagtegaal et al. 2005 rectum along with surrounding tissues In addition to the resection of the primary tumor, the resection of colorectal liver metastases in selected patients has been slowly adopted as the standard of care during the last 20 years (Tomlinson et al. 2007). The criteria for resectability are not standardized and depend on technical aspects (Schmoll et al. 2012). In a series of 612 consecutive patients, the median CSS was 44 months after the resection and the survival curve reached a plateau 10 years after the operation, representing a cure rate of 17% (Tomlinson et al. 2007). 2.6.2 Neoadjuvant treatment for rectal cancer Neoadjuvant therapy for locally advanced rectal cancer may reduce the risk of local relapse, improve resectability, help to preserve the sphincter function, and help to avoid stoma (Glimelius et al. 2013, Kapiteijn et al. 2001). The European Society for Medical Oncology (ESMO) guidelines recommend preoperative RT or CRT for locally advanced rectal tumors, with exact criteria varying depending on tumor location (Glimelius et al. 2013, Schmoll et al. 2012). There are two 58 main modalities of treatment, short-course RT with 5×5 Gy followed by immediate surgery and long-course CRT with 50.4 Gy in 25-28 fractions with surgery after a 4- to 8-week break (Glimelius et al. 2013). 2.6.3 Adjuvant treatment for colorectal cancer Adjuvant treatment is given after surgery to colon cancer patients with a high risk of recurrence (Schmoll et al. 2012). For several decades, leucovorin and 5Fluorouracil (5-FU) have formed the basis of the treatment, and their combination has been shown to reduce recurrence rate by 41% and overall death rate by 33% relative to surgery alone in stage III disease (Moertel et al. 1990). The FOLFOX regimen is based on leucovorin, 5-FU, and oxaliplatin, and improves the survival relative to leucovorin and 5-FU alone (André et al. 2004). The role of adjuvant therapy in stage II colon cancer is controversial. The ESMO guidelines recommend therapy only for high-risk stage II patients, e.g., to those with pT4 depth of invasion or the presence of vascular or lymphatic or perineural invasion (Schmoll et al. 2012). The available data from randomized trials for different adjuvant treatment modalities for rectal cancer after preoperative RT or CRT are partly controversial (Glimelius et al. 2013, Schmoll et al. 2012). Although the level of scientific evidence for sufficient benefit is much lower than in colon cancer, 5-FU alone or in combination with leucovorin can be given to stage III or high-risk stage II patients (Glimelius et al. 2013). During the last decade, the treatment of metastatic CRC has advanced (Kemeny 2013). In addition to the increasing recognition of the potential to resect liver metastases, there are now more options in chemotherapy. The standard treatment regimens are still based on 5-FU but also new monoclonal antibodies have been introduced to the treatment and have been shown to bring advances in the survival (Schmoll et al. 2012). The monoclonal antibodies used in the treatment of metastatic CRC include bevacizumab (Hurwitz et al. 2004), a monoclonal antibody against VEGF, and cetuximab (Cunningham et al. 2004) and panitumunab (Van Cutsem et al. 2007), monoclonal antibodies against EGFR. 59 60 3 Aims of the study The present work focused on the characteristics and the significance of immune cell infiltration and inflammatory biomarkers in CRC. The specific objectives were: 1. 2. 3. 4. 5. 6. To test the applicability of a color channel separation based image analysis method for immune cell counting in CRC (I) and the applicability of CLR density counting for the evaluation of CLR (III). To determine the interrelationships between different inflammatory cell types within colorectal tumors (II, III). To evaluate the prognostic value of T cells and other tumor-infiltrating immune cells (I, II). To enlighten the characteristics and significance of CLR (III). To characterize the associations between serum MMP-8 levels and CRCassociated inflammatory infiltrate (IV). To assess the sensitivity and specificity of serum MMP-8 in discriminating the CRC patients from healthy controls (IV). 61 62 4 Materials and methods 4.1 Patients (I-IV) The studies were based on two independent cohorts of CRC patients, and Cohort 2 was further divided into Cohort 2a (no preoperative treatments) and Cohort 2b (preoperative RT or CRT) (Table 24). The studies were approved by the Ethical Committee of Oulu University Hospital. Table 24. Patient characteristics. Characteristic Cohort 1 (n=418) Cohort 2a (n=117) Cohort 2b (n=32) Studies I, III II, III, IV III, IV Time of operation 1986–1996 2006–2010 2006–2010 Prospective recruitment No Yes Yes Tissue microarray No Yes Yes Serum samples No Yes Yes Age, mean (SD) 67.7 (12.4) 67.7 (11.2) 63.4 (10.3) Male 200 (47.8%) 58 (49.6%) 22 (68.8%) Female 218 (52.2%) 59 (50.4%) 10 (31.2%) Yes 0 (0%) 0 (0%) 32 (100%) No 418 (100%) 117 (100%) 0 (0%) Proximal colon 130 (31.1%) 49 (41.9%) 0 (0%) Distal colon 116 (27.8%) 28 (23.9%) 0 (0%) Rectum 172 (41.1%) 40 (34.2%) 32 (100%) 1 101 (24.2%) 16 (13.8%) 5 (15.6%) 2 246 (58.9%) 86 (74.1%) 22 (68.8%) 3 71 (17.0%) 14 (12.1%) 5 (15.6%) I 90 (21.5%) 19 (16.5%) 8 (25.0%) II 180 (43.1%) 46 (40.0%) 9 (28.1%) III 98 (23.4%) 32 (27.8%) 14 (43.8%) IV 550 (12.0%) 18 (15.7%) 1 (3.1%) MMR Proficient 362 (91.0%) 105 (90.5%) 32 (100%) MMR Deficient 36 (9.0%) 11 (9.5%) 0 (0%) Gender Preoperative radiotherapy or chemoradiotherapy Location of tumor Grade Stage Mismatch repair (MMR) screening status 63 Cohort 1 was comprised of 418 (89.7%) of a consecutive series of 466 CRC patients, operated on in Oulu University Hospital between 1986 and 1996 (Hörkkö et al. 2006). The cases were retrieved from the archives of the Department of Pathology, Oulu University Hospital, and regraded by TNM6 classification for these studies (Sobin & Wittekind 2002). Forty-eight (10.3%) patients were excluded from the studies because of inadequacy of the material to reliably conduct TNM staging and other histological evaluations. Of all the included patients, 350 (83.7%) had 60-month follow-up data from the Finnish Cancer Registry. All 418 patients were included in Study III, whereas 235 (67.1%) of 350 patients with follow-up were selected for Study I. A total of 344 patients were operated on in Oulu University Hospital between 2006 and 2010. Of these patients, 149 (43.3%) prospectively recruited patients who had signed an informed consent for the study were included in Cohort 2. Patients with earlier or simultaneously diagnosed other malignant diseases were excluded. Preoperative staging of Cohort 2 was done by computer tomography and the local staging of rectal cancer was done by magnetic resonance imaging. Thirtytwo patients with locally advanced rectal cancer in Cohort 2 received preoperative RT or CRT: 24 of them received a short-course RT, whereas eight received a longcourse CRT or RT. Cohort 2 was divided into Cohort 2a (no preoperative treatments) and Cohort 2b (preoperative RT or CRT), due to the potential effects of preoperative treatments on the histological properties of the tumors (Nagtegaal et al. 2002). Cohort 2a was included in studies II, III, and IV, while Cohort 2b was included in studies III and IV. Clinical details of the patients were collected from the clinical records (age, gender, treatments, recurrences) (Cohort 1, Cohort 2) and by a questionnaire (height, weight, medication and previous illnesses) (Cohort 2). Height and weight were used to calculate body mass index (BMI). 4.2 Control group (IV) Eighty-three healthy age- and sex-matched controls were recruited for Cohort 2. Controls younger than 65 years were healthy blood donors (Finnish Red Cross, Oulu, Finland), and those aged 65 years or more were recruited from patients undergoing cataract surgery in Oulu University Hospital. Because of the regulations in blood donation, the exclusion criteria for blood donor controls included too low or high hemoglobin levels (outside 135–195 g/L for men and 64 125–175 g/L for women), trauma or operation during the preceding 4 months, chronic diseases like coronary artery disease, stroke or cancer, organ transplantation, and acute infections. 4.3 Histopathological analysis (I-IV) Samples from surgical specimens had been fixed in 10% buffered formalin and embedded in paraffin. Five-micrometer sections had been cut and stained with H&E. 4.3.1 Stage and Grade (I-IV) The staging for both cohorts was performed according to TNM6 (Sobin & Wittekind 2002) and the grading of differentiation according to WHO criteria (Hamilton et al. 2010). On average, 15 (median 12, interquartile range 8–19, range 0–62) lymph nodes were examined in Cohort 2, whereas this information was not available for Cohort 1. 4.3.2 Necrosis (IV) The H&E stained sections were graded for the amount of necrosis using a threegrade scale: NG0 denoted rare areas of necrosis, NG1 denoted frequent small areas of necrosis, and NG2 denoted broad areas of necrosis. The evaluations were done independently by two researchers after which the cases with divergent evaluations were viewed again and mutual agreement was achieved. 4.3.3 Tumor budding (I) Tumor budding was evaluated as present when narrow strands or clusters of cancer cells of one to three cells in width were observed extending beyond the tumor margin (Hörkkö et al. 2006). 4.3.4 Peritumoral inflammatory reaction (II-IV) Peritumoral inflammatory reaction was evaluated from H&E stained sections utilizing the Klintrup-Mäkinen (2005) method. A score of 0 was given when there was no increase of inflammatory cells, 1 denoted mild and patchy increase of 65 inflammatory cells, a score of 2 was given when inflammatory cells formed a band-like infiltrate at the invasive margin with some evidence of destruction of cancer cell islets, and a score of 3 denoted a very prominent inflammatory reaction with frequent destruction of cancer cell islets. The scores were classified as low-grade (scores 0 and 1) and high-grade (scores 2 and 3). 4.3.5 Colorectal cancer associated lymphoid reaction (III-IV) CLR was defined as lymphoid structures surrounding the primary tumors, not associating with either mucosa (thus excluding mucosa-associated lymphoid tissue) or pre-existing lymph nodes. Its extent was evaluated according to the criteria established by Graham and Appelman (1990) (III, IV), where cases were classified into three classes: CLR0 (no reaction) denoting no or at most one single lymphoid aggregate in all tumor sections, CLR1 (mild reaction) defined as occasional lymphoid aggregates with rare or absent germinal centers, and CLR2 (intense reaction) denoting numerous lymphoid aggregates with germinal centers. A more detailed classification was adopted (III), based on counting the lymphoid follicles. CLR density was defined as “the number of CLR follicles/the length of the invasive front”. Lymphoid follicles with germinal centers were counted separately. The average diameter of the lymphoid follicles was determined using a scale placed on the ocular lens. The histological layer (submucosa/muscularis propria/serosa) with the highest concentration of lymphoid follicles with or without germinal centers was determined. 4.4 Immunohistochemistry (I-IV) 4.4.1 Tissue microarray (II, III) For Cohort 2, a TMA was constructed to facilitate the analysis of densities of multiple inflammatory cell types. The H&E slides were used to select the locations for the sampling. Depending on the size of the tumor, a total of 1–4 (median 3) cores of 3.0 mm diameter were manually sampled for each case yielding an overall tumor area of 7.1–28.3 mm2. One to three (median 2) of these cores were acquired from the IM of the tumors containing the point of deepest invasion and the rest were sampled from the CT. The necrotic areas were avoided. 66 4.4.2 Protocols (I-IV) Section of 3.5 µm cut from paraffin-embedded specimens were deparaffinized in xylene and rehydrated through graded alcohols. For antigen retrieval, the sections were pre-treated with Tris-EDTA buffer (pH 9.0) in a microwave oven at 800 W for 2 min and at 150 W for 15 min. After cooling down to room temperature and neutralizing endogenous peroxidase activity, the sections were incubated at room temperature with primary antibodies (Table 25). Bound antibodies were detected using the EnVision system (Dako, Copenhagen, Denmark), except for MLH1, which was detected using the NovoLink Polymer detection system (Leica Biosystems, Newcastle, UK). 3,3’-Diaminobenzidine (DAB) was used as the chromogen and hematoxylin as the counterstain. Table 25. Antibodies and protocols used in immunohistochemistry. Cell type Antigen Type Clone Dilution Incubation Studies T cells CD3 monoclonal Novocastra Manufacturer PS1 1:50 30 min I, II, III Cytotoxic T cells CD8 monoclonal Novocastra 4B11 1:200 30 min II, III Regulatory T cells FoxP3 monoclonal Abcam 236A/E7 1:100 30 min I, II, III B cells CD20 monoclonal DAKO L26 1:1000 30 min II, III Macrophages CD68 monoclonal DAKO PG-M1 1:100 30 min II, III Neutrophils Neutrophil monoclonal DAKO NP57 1:200 30 min II, III monoclonal DAKO AA1 1:2000 30 min II, III elastase Mast cells Mast cell tryptase Mature DCs CD83 monoclonal Abcam 1H4b 1:25 2 hr II, III Immature DCs CD1a monoclonal DAKO O10 1:200 30 min II, III Proliferating cells Ki-67 monoclonal DAKO MIB-1 1:300 30 min III MLH1+ cells MLH1 monoclonal BD-Pharmingen G168-15 1:200 1 hr II, III, IV MSH2+ cells MSH2 monoclonal BD-Pharmingen G219- 1:150 1 hr II, III, IV 1:500 1 hr IV 1129 MMP-8+ cells MMP-8 polyclonal non-commercial 4.4.3 Analysis of Immunohistochemistry (I-IV) MMR Enzymes (II-IV) The expression was evaluated positive if there was any staining in the cancer cell nuclei and negative if there was no staining in any of the cancer cell nuclei. Normal proliferating tissue, e.g., crypt epithelium or germinal centers of 67 lymphoid follicles, was used as an internal positive control. Cancer cells devoid of the expression of either MLH1 or MSH2 were considered mismatch repair (MMR) enzyme deficient, while others were considered MMR-proficient. MLH1 and MSH2 immunohistochemistry for MSI screening has earlier been attributed 92.3% sensitivity and 100% specificity in a series of 1,144 cases (Lindor & Burgart 2002). Computer-based analysis to calculate tumor infiltrating immune cells (I-III) Images were captured from the IM and from the stromal and intraepithelial parts of CT with an Olympus DP25 camera (Olympus, Center Valley, PA) attached to a Nikon Eclipse E600 microscope (Nikon, Tokyo, Japan) using 20× and 10× objectives. The computer-assisted image analyses were conducted using ImageJ v1.44 (Abramoff et al. 2004), a Java-based open source image processing software. The cell counting method consisted of six phases (Fig. 7). The commands were recorded as a macro for ImageJ enabling a continuous, automated analysis. To facilitate quality control, the macro was programmed to save a result image for each calculation (Fig. 8). 68 a b c d e f g Fig. 7. Method used for counting immune cells. (a) Original image. (b–g) The cell counting method consisting of six phases. (b) Rolling ball method for background subtraction (Sternberg 1983). (c) Color deconvolution for the separation of DAB layer (Ruifrok & Johnston 2001). (d) Brightness threshold for the acquisition of a binary image. (e) Gaussian blur for smoothening of the threshold. (f) Watershed method for the segmentation of cells touching each other (Beucher & Meyer 1993). (g) Analyze particles tool for calculating the cell count based on the size and shape of the objects (Abramoff et al. 2004). An ellipse indicates a counted cell. 69 Fig. 8. Result image of the image analysis. Original captured picture (intratumoral + CD3 T cells) is presented as the upper image, and the counted cells have been marked with dark grey shading in the lower image. 70 4.5 Serum analyses (IV) Preoperative serum samples of Cohort 2 and serum samples of their age- and gender-matched controls were centrifuged, after which the supernatants were collected and stored at -70°C until analysis. Serum MMP-8 concentrations were determined by a time-resolved immunofluorometric assay (IFMA) (Medix Biochemica, Kauniainen, Finland) according to the manufacturer’s instructions with a serum dilution of 1:5 (Tuomainen et al. 2007). TIMP-1 ELISA (R&D Systems, Minneapolis, MN) was performed according to the manufacturer’s instructions with 1:300 dilutions of the serum. 4.6 Measurement of intra- and inter-observer variation (I, III) Peritumoral T cells of 34 randomly selected cases were used in testing the validity of the automated computer-based cell counting method (I). The positive cells were counted with both automated cell counting method and manually from the same images by three independent evaluators. Pearson correlation coefficients were used in the measurement of the accuracy of the computer-based immune cell counting. To test the reproducibility of the evaluation of CLR density (III), two observers independently conducted CLR density evaluations on 43 randomly selected patients. The agreement was measured for both CLR density as a continuous variable (Pearson r) and CLR density as two-tiered variable using a cut-off of 0.38 follicles/mm (κ score). 4.7 Statistical analyses (I-IV) Normally distributed continuous variables were presented as mean (standard deviation), whereas other continuous variables were presented as median (interquartile range). The statistical analyses were carried out using statistical analysis software PASW Statistics 18 (IBM, Chicago, IL) (IV) or IBM SPSS Statistics 19 (I-III). Statistical significances of the associations between categorical and continuous variables were analyzed by Mann-Whitney U test (comparing two classes) or Kruskal-Wallis test (comparing three or more classes) (II, III, and IV). The associations between two categorical variables were analyzed by crosstabulation and χ2 test or Fisher’s exact test (II and IV). Pearson correlation coefficients (r) were used to assess the correlations between two 71 normally distributed continuous variables (I-IV). Multiple linear regression analysis was used to model the relationship between two or more explanatory variables and a response variable (IV). The Kaplan-Meier method and log-rank test, as well as Cox’s proportional hazards regression models were used in survival analyses (I, II, and III). In all the tests, a two-tailed, exact p value less than 0.05 was considered statistically significant. Receiver operating characteristics (ROC) analysis is a method where sensitivity and 1-specificity values are plotted at various cut-off points to evaluate the discriminatory capacity of a marker and to determine optimal threshold values (Zlobec et al. 2007b). It was used in determining optimal cut-off points with the shortest distance to the coordinate (0,1) for the categorization of continuous variables (I-IV). Hierarchical clustering is a method which aims to identify the structure and relationships of groups based on a multivariate profile. In hierarchical clustering of different inflammatory cell types, the nearest neighbor method with standardized squared Euclidean distance was applied (II), denoting that the clusters were sequentially combined into larger clusters and two clusters separated by the shortest distance were combined at each step. The squared Euclidian distance method increased the importance of large distances while weakening the importance of small distances. 72 5 Results 5.1 New methods for the evaluation of immune cell reaction In these studies, two new methods for the evaluation of immune cell infiltration in CRC were adopted and validated. 5.1.1 Computer-based immune cell counting To test the accuracy of the new method for automated computer-based immune cell counting (I), a median of five (range, 2–11) images were obtained from the IM and a median of four (range, 2–7) from CT-S in the samples of 34 CRC cases. The CD3+ cell densities were counted with both automated cell counting method and manually from the same images by three independent evaluators. The automated cell counting method achieved an almost perfect correlation with manual cell counting from the same images (Pearson r=960–0.987), indicating that the automated counting was accurate. The slight variation was found to be a result of either weakly positive immunoreaction in the T cells or background staining. Three additional series of images were captured from the same sections to map the effect of image capturing on the cell counts and thus evaluate the overall reproducibility of the immune cell assessment. Although more variation was observed, the correlations between different image series were still excellent (Pearson r = 0.832–0.934), indicating that the number of images captured for each case was adequate. 5.1.2 CLR density To estimate the reproducibility of the new method for the evaluation of CLR, two observers independently calculated CLR density (the number of CLR follicles/the length of the invasive front) on 43 randomly selected patients. After one month, another CLR density calculation was performed to evaluate the intra-observer variation. The intra-observer agreement (r=0.970; κ=0.814) and the inter-observer agreement (r=0.910–0.93; κ=0.720–0.813) were excellent, indicating that the CLR density evaluation was reproducible. 73 5.2 Immune cell infiltration in colorectal cancer 5.2.1 Characteristics of immune cell infiltration Of the 117 cases in Cohort 2a, 65 (55.6%) showed a high-grade KlintrupMäkinen score, signifying a band-like inflammatory infiltrate at the IM with evidence of cancer cell destruction (II). Immunohistochemistry was used to further characterize the inflammatory infiltrate at the IM and in the CT (Fig. 9). The CD3+ T cells were the most frequent both at the IM and in the CT, followed by CD68+ cells, CD8+ T cells, and FoxP3+ T cells. At the IM, the median CD8+ and FoxP3+ T-cell counts were 30.4% and 26.7% of the amount of CD3 + T cells, respectively, and in the CT-S, 19.3% and 29.8%, respectively. The inflammatory infiltrate at the IM was, in general, heavier than that in the CT-S. a b c d e f g h Fig. 9. Representative examples of immunohistochemical determination of eight types + of immune cells at the invasive margin of colorectal cancer. (a) CD3 T cells. (b) CD8 + + + T cells. (c) FoxP3 T cells. (d) CD68 macrophages. (e) CD83 dendritic cells. (f) CD1a + + dendritic cells. (g) Tryptase mast cells. (h) Elastase neutrophils. 74 + + When evaluating CLR (III), at least one peritumoral lymphoid follicle was recognized in 411 of 418 (98.3%) patients in Cohort 1 and 147 of 149 (98.7%) patients in Cohort 2 (Fig. 10). No granulomas were detected. CLR density showed substantial positive correlations with the average follicle diameter (Cohort 1, Pearson r=0.515; Cohort 2, Pearson r=0.655) and the density of lymphoid follicles with germinal centers (Cohort 1, r=0.614; Cohort 2, r=0.814). In most cases, the highest density of the lymphoid aggregates was located at the border of muscularis propria and serosa (Cohort 1, n=250, 60.8%; Cohort 2, n=81, 55.1%) or in muscularis propria (Cohort 1, n=123, 29.9%; Cohort 2, n=50, 34.0%). Fig. 10. Representative example of intensive colorectal cancer associated lymphoid reaction (CLR), which is defined as lymphoid aggregates surrounding the tumor. The composition of CLR was analyzed by immunohistochemistry, and CD20+ B cells had the highest average positive area percentage (60.8% of the follicle), followed by CD3+ T cells (38.0%), CD68+ cells (11.1%), FoxP3+ T cells (0.40%) and CD83+ mature DCs (0.36%). Ki-67 immunohistochemistry indicated high 75 proliferation rate at the germinal centers and also a few proliferating cells in the majority of the lymphoid aggregates without germinal centers. 5.2.2 Interrelationships between different immune cell types The interrelationships between different inflammatory cells were analyzed by calculating Pearson’s correlation coefficients and by hierarchical clustering of the inflammatory cell markers in Cohort 2a (II). Different inflammatory cells had high positive correlations with each other, except for mast cells and CD1a+ immature DCs, which also clustered furthest from T cells in hierarchical clustering (Fig. 11). The CD83+ mature DCs clustered with T cells. Immune cell counts of each cell type within different tumor locations showed substantial concordance, with Pearson r varying from 0.434 (CD83) to 0.714 (CD3). 0 5 10 15 20 25 CD3, CT-IEL CD8, CT-IEL CD8, CT-S FoxP3, IM FoxP3, CT-S CD8, IM CD3, IM CD3, CT-S CD83, IM CD83, CT-S CD68, IM CD68, CT-S Neutrophil, IM Neutrophil, CT-S CD1a, IM CD1a, CT-S Mast cell, IM Mast cell, CT-S Fig. 11. Hierarchical clustering of eight immune cell types in different tumor locations. Abbreviations: CT: tumor center; IEL: intraepithelial; IM: invasive margin; S: stromal. In cohort 2a, a high Klintrup-Mäkinen score associated with higher densities of CD3+, CD8+, and FoxP3+ T cells, CD68+ cells, CD83+ mature DCs, and 76 neutrophils (II). Although based on the evaluation of the inflammatory reaction at the IM, the classification had also high correlation with the densities of inflammatory cells in CT. In cohort 2a, CLR density had positive correlations with the densities of CD83+ mature DCs and T cells (CD3+, CD8+ and FoxP3+), whereas neutrophils, mast cells, CD68+ macrophages and CD1a+ immature DCs did not show significant associations with CLR density (III). 5.2.3 Relationships between immune cell infiltration and clinical and pathological variables In Cohort 2a (II), the Klintrup-Mäkinen score correlated inversely with stage (p=1.2E−3). In a more detailed analysis, higher TNM stage, in particular stage IV, associated especially with lower densities of FoxP3+, CD3+, and CD8+ T cells, as well as CD83+ DCs (Table 26). Conversely, mast cells, stromal CD68+ cells, stromal CD1a+ cells, and stromal neutrophils did not have significant associations with stage. CLR density (III) showed a tendency towards higher values in lower stages in Cohort 2a (Table 26). In Cohort 1, high CLR density associated significantly with low stage (p=3.2E−3). 77 Table 26. Immune cell infiltration in different stages. Cell type and location TNM Stage p value I (n=19) II (n=46) III (n=32) IV (n=18) 1120.2 (668.3– 566.9 (397.1– 573.5 (307.9– 205.2 (146.1– 1253.3) 867.7) 927.8) 434.4) 735.1 (412.7– 517.7 (223.9– 379.6 (225.1– 223.6 (110.8– 1187.4) 858.6) 651.5) 510.3) Immune cells CD3, IM CD3, CT-S CD3, CT-IEL 72.1 (23.9–159.4) 26.1 (10.2–97.8) 22.7 (14.1–78.7) 10.6 (4.8–35.6) CD8, IM 265.5 (159.4– 184.1 (100.2– 149.5 (74.1– 495.9) 381.1) 436.1) 175.9 (42.2– 98.8 (55.1–210.4) 91.7 (26.8–237.8) 25.6 (13.5–84.7) CD8, CT-S 1.5E−5 6.6E−3 2.8E−3 95.0 (25.0–176.3) 4.7E−3 8.5E−3 260.3) CD8, CT-IEL 23.9 (10.6–112.6) 22.0 (6.6–63.3) 27.3 (4.2–59.5) 6.2 (1.7–19.0) FoxP3, IM 276.7 (222.8– 158.8 (85.3– 147.7 (74.8– 54.2 (27.0–106.6) 1.6E−7 416.2) 312.7) 280.2) 250.9 (156.5– 142.8 (91.0– 121.7 (63.7– 389.2) 381.9) 284.3) 619.0 (269.9– 647.1 (390.1– 495.9 (292.8– 361.1 (246.8– 975.4) 899.9) 778.0) 544.5) 277.9 (186.4– 410.3 (224.2– 393.7 (187.6– 311.3 (189.3– 607.2) 597.0) 637.5) 476.3) 16.2 (11.14– 10.4 (1.8–21.0) 10.6 (5.6–21.8) 4.0 (2.0–11.9) 0.012 5.8 (1.0–15.3) 0.208 FoxP3, CT-S CD68, IM CD68, CT-S CD1a, IM 42.5 (20.2–73.6) 0.012 9.7E−6 0.055 0.629 25.32) CD1a, CT-S 10.6 (7.6–22.9) 10.1 (2.7–15.3) 7.3 (3.5–12.9) CD83, IM 14.1 (7.0–21.7) 6.3 (3.24–11.5) 8.57 (2.26–12.7) 2.3 (0.88–6.51) 3.2E−4 CD83, CT-S 7.0 (2.6–16.3) 4.1 (1.7–7.7) 5.4 (2.7–8.7) 8.6E−3 Mast cell tryptase, 65.7 (29.9–105.5) 38. (24.4–82.1) 1.9 (0.55–4.9) 39.9 (24.0–69.8) 39.0 (24.2–50.1) 0.299 56.3 (35.2–91.4) 36.1 (17.4–72.7) 37.2 (25.4–60.5) 39.3 (20.5–46.6) 0.163 98.5 (11.7–397.9) 60.7 (14.1–273.5) 52.1 (10.8–291.5) 9.1 (4.8–40.8) 0.079 56.3 (9.4–123.1) 39.9 (10.6–166.3) 32.5 (7.6–100.8) 25.3 (8.79–128.6) 0.545 IM Mast cell tryptase, CT-S Neutrophil elastase, IM Neutrophil elastase, CT-S Colorectal cancer associated lymphoid reaction (CLR) CLR Density 0.44 (0.14–0.93) 0.59 (0.20–0.95) 0.34 (0.15–1.00) 0.27 (0.15–0.69) 0.234 2 Based on cohort 2a. Numbers indicate “median (interquartile range) number of cells/mm ” for immune cells and “median (interquartile range) number of lymphoid follicles/mm of invasive front” for CLR. P values are for Kruskal-Wallis test. 78 In cohort 2a, the MMR-deficient cases showed a trend towards a higher KlintrupMäkinen score (p=0.062), and in a detailed analysis, MMR deficiency associated with increased amounts of CD3+ (IM: p=0.022; CT-S: p=0.223; CT-IEL: p=0.019) and CD8+ (IM: p=0.014; CT-S: p=0.025; CT-IEL: p=0.013) T cells (II). MMR deficiency also notably correlated with CLR density (III) (Cohort 1: p=5.8E−5; Cohort 2a: p=5.2E−3). 5.2.4 Prognostic value Eighty (68.4%) of the 117 patients in Cohort 2a, of whom 15 (18.8%) had a recurrence, had 24-month follow-up data for DFS (II), while 350 (83.7%) of the 418 patients in Cohort1 had 60-month follow-up data for CSS (I, III). Kaplan-Meier analysis in Cohort 2a indicated that four-tiered KlintrupMäkinen score (p=0.024), CD3+ T cells at the IM (p=0.037), as well as FoxP3+ T cells (IM: p=0.049; CT-S: p=2.7E−3) had significant associations with improved DFS (II). However, the short follow-up did not enable a construction of sensible multivariate survival models (II). In Cohort 1, high CLR density (≥0.38 follicles/mm) (p=4.5E−6) and high Klintrup-Mäkinen score (p=1.4E−12) were associated with increased CSS (III). Combined four-tiered evaluation of CLR density and Klintrup-Mäkinen score improved the discriminatory capacity of the individual markers (p=1.6E−13). Cox regression analysis indicated that the CLR density (HR 0.54, 95% CI 0.37–0.80) and Klintrup-Mäkinen score (HR 0.43, 95% CI 0.27–0.67) had prognostic value independent of TNM classification, WHO grade, tumor location, and MMR screening status. In a subset of 235 patients in Cohort 1, it was established that CD3+ T cell density has prognostic value (HR 0.49, 95% CI 0.28–0.85) independent of TNM classification, WHO grade, tumor location, and tumor budding (I). However, a comparative analysis with Klintrup-Mäkinen score and CLR density in a subset of 300 patients in Cohort 1 suggested that CD3+ T cells provide no additional value relative to Klintrup-Mäkinen score and CLR density (III). 79 5.3 Systemic inflammatory biomarkers in colorectal cancer 5.3.1 Serum MMP-8 The median serum MMP-8 levels of the patients in Cohort 2a was more than three times higher than that of the age- and gender-matched healthy controls (63.0 vs. 17.2 ng/mL, p=1.5E−9) (IV). A ROC analysis indicated an area under the curve (AUC) of 0.751 (95% CI 0.685–0.817) in separating the patients from the controls. Using a cut-off value of 63.4 ng/mL, the specificity was 90.4% and the sensitivity 50.0%. Serum MMP-8 levels were next correlated with clinicopathological characteristics. Higher levels were observed in advanced stage (p=4.5E−4). Of the inflammatory parameters studied, higher serum MMP-8 levels associated with a lower Klintrup-Mäkinen score (p=0.041) and lower-grade CLR (p=0.0057). A positive correlation was observed between serum MMP-8 levels and the extent of tumor necrosis (p=0.0024). Of the hematological parameters, high S-MMP-8, most notably, associated with high blood neutrophil count (Pearson r=0.523). In a multiple linear regression model, the four best predictors of high serum MMP-8 levels were high blood neutrophil count, the presence of distant metastases, lowgrade CLR and low BMI. Possible sources of elevated serum MMP-8 levels in CRC were further evaluated by immunohistochemical examination of the tumor specimen. In all five tumor samples analyzed, necrotic areas and neutrophils were constantly positive for MMP-8. The expression of MMP-8 in a few cancer cells, restricted to 0.1–5% of the cells, was identifiable in two of the five tumor samples. The cancer cells expressing MMP-8 were not located in any particular part within the tumors. Epithelial cells in normal colon mucosa did not express MMP-8. 5.3.2 Other markers The correlations between CLR density and several markers of systemic inflammation were studied in Cohort 2a (III). However, no significant correlations were found between CLR density and blood leucocyte counts (p=0.159-0.865), CRP (p=0.064), and GPS (p=0.441). 80 6 Discussion For the past decades, the prognostication in CRC has been based on TNM staging (Sobin et al. 2009). In patients operated on in the 1990s, five-year OS was 65%, ranging from 90% in stage I to less than 10% in stage IV (O’Connell et al. 2004). However, molecular heterogeneity of the disease warrants the search for additional, complementary prognostic markers (Jass 2007b). It has been established that the immune system and immune cell infiltration influence cancer outcome (Fridman et al. 2012, Shankaran et al. 2001), and there is an international initiative to incorporate immune cell infiltration into cancer classification (Galon et al. 2012, 2014). Accurate analysis methods are important for reliable and reproducible results. The present studies validate new, objective methods for the analysis of immune cell infiltration and provide insight into the significance of various immune cell types and inflammatory markers in CRC. 6.1 New methods for the evaluation of immune cell reaction A new method for immune cell counting — based on separating DAB and hematoxylin color layers with ImageJ, a freely available image analysis software — was described and was found accurate (I). The quantitative evaluation of CLR density was also established to show excellent intra-observer and inter-observer agreement (III). 6.1.1 Computer-based immune cell counting Earlier, studies have utilized a variety of methods in the analysis of immune cell infiltration in CRC, each with a number of advantages and disadvantages. Manual classification into two to four categories has been commonly used and is quick to conduct (Forssell et al. 2007, Graham & Appelman 1990, Jass et al. 1987, Naito et al. 1998). However, it is subjective and the lack of continuous variables also limits the applicability of statistical methods including ROC analysis and linear regression and can lead to difficulties in comparing the results of different studies (Walker 2006, Zlobec et al. 2007b). Manual cell counting from captured images is exact, if performed carefully, and it was used as a method to compare with automated computer assisted cell counting. It was found that cell counts determined by automated cell counting had nearly perfect correlation with the manual cell counts from the same images (Pearson r=960-0.987), indicating that 81 the adopted counting method was accurate. Supporting the rationale behind the computer-assisted counting relative to manual counting, computer-assisted counting needs only to be supervised whereas manual counting is time-consuming (Ong et al. 2010). Since the late 1980s when the first methods were developed (Bacus et al. 1988), the precision of commercially available computer-based platforms for the analysis of immunohistochemistry has been proven in a number of applications, including the analysis of estrogen receptor status in breast cancer (Gokhale et al. 2007), cytokeratin expression in CRC (Ong et al. 2010), immune cell counting in CRC (Pagès et al. 2005) and Hodgkin lymphoma (Lejeune et al. 2008). However, the availability of commercial automated analysis equipment is still limited. Therefore, freely available methods, such as the one that was adopted and validated in Study I, offer a valuable opportunity to improve the accuracy of the analysis of immunohistochemistry. ImageJ is free image analysis software that is widely used in medical research (Abramoff et al. 2004), including analysis of radiography (Sustercic & Sersa 2012, Tharwat et al. 2014) and immunohistochemistry (Ozerdem et al. 2013, Tuominen et al. 2012). It offered a versatile platform to combine functions that had earlier been described into a macro, which was capable of accurately counting immune cell densities in the CRC specimens. A slightly modified version of the macro would also be able to count the percentage of positive nuclei. Indeed, although independently compiled, Immunoratio — a plugin for ImageJ that was validated for the analysis of estrogen receptor, progesterone receptor and Ki-67 — is based on many of the same functions that were used in the immune cell counting macro validated in this study (Tuominen et al. 2010). Moreover, Immunomembrane — a plugin for ImageJ capable of the evaluation of membranous human epidermal growth factor receptor 2 staining — was recently released (Tuominen et al. 2012). Thus, a variety of ImageJ-based tools for the analysis of immunohistochemistry now exist and image analysis based on the same approaches can also be carried out using other freely available image analysis programs like CellProfiler (Lamprecht et al. 2007). The potential sources of error in new methods need to be identified (Walker 2006) (Table 27). In Study I, the minor differences in the cell counts by the computer-aided method and manual counting from the same images mostly resulted from either substantial background staining or weak positivity in the desired cells. Those differences could be detected by the revision of the result 82 images created by the cell counting macro (Fig. 8), which is an important part of quality control when utilizing the automated cell counting method. Table 27. Potential sources of error in automated cell counting and methods of their prevention. Type of error Source Method of prevention 1. Immunopositive cell counted as A. Weak positivity in the Standardized and validated staining negative protocols, e.g., specific antibodies, desired cells standardized section thickness and incubation practices, and proper positive and negative controls 2. Immunonegative cell counted B. Too high upper threshold Adequate familiarization with the value for cell size counting method and its calibration C. Too low upper threshold Adequate familiarization with the value for brightness counting method and its calibration A. Background staining Standardized and validated staining as positive protocols B. Too low lower threshold Adequate familiarization with the value for cell size counting method and its calibration C. Too high upper threshold Adequate familiarization with the value for brightness counting method and its calibration 3. Part of background counted as A. Background staining Standardized and validated staining positive protocols B. Too high upper threshold Adequate familiarization with the value for brightness counting method and its calibration 4. Two or more cells touching Inadequate segmentation Development of an improved each other counted as one method segmentation method; However, rarely 5. One cell counted as two or Inadequate segmentation Development of an improved more cells method segmentation method; However, rarely causes significant error causes significant error 6.1.2 CLR density CLR density calculation was described as a new, objective method to quantitatively evaluate the transmural lymphoid reaction surrounding colorectal tumors (III). As computer-based immune cell counting, it benefits from objectivity and the applicability of statistical methods such as ROC analysis and linear regression (Walker 2006, Zlobec et al. 2007b). Earlier, qualitative criteria suggested by Graham and Appelman (1990) have been frequently utilized, but our 83 results indicate that the prognostic value of CLR density is superior to GrahamAppelman criteria. Recently, also another suggestion for objective criteria for the evaluation of CLR was made (Ueno et al. 2013). The researchers found that the size of the largest lymphoid aggregate of 1 mm or higher strongly associated with lower recurrence and improved survival independent of tumor stage. This method yielded a κ score of 0.67 for inter-observer agreement, which was comparable to that of CLR density in this study (inter-observer κ=0.720–0.813). Future studies are required to evaluate whether a combined evaluation of CLR density and follicle size can improve the prognostic value relative to individual evaluations. 6.2 Immune cell infiltration in colorectal cancer The densities of eight inflammatory cell types — including markers of both adaptive (CD3, CD8, and FoxP3) and innate immunity (CD68, neutrophil elastase, and mast cell tryptase), as well as antigen-presenting cells (APCs) (CD1a, CD68, and CD83) serving as a link between the two (Banchereau et al. 2000) — were calculated in Cohort 2 utilizing the computer-assisted method to enlighten the interrelationships between different types of tumor-infiltrating immune cells in CRC (II). Also CLR density was correlated with other markers (III). The results indicated that there are high positive correlations between the densities of tumor-infiltrating CD3+, CD8+, and FoxP3+ T cells, CD83+ DCs, CD68+ macrophages, and neutrophils, whereas CD1a+ DCs and mast cells show weaker correlation with other cell types. 6.2.1 T cells in colorectal cancer T lymphocytes, the hallmark of cell-mediated adaptive immunity, are considered essential in tumor immunosurveillance (Schreiber et al. 2011, Shankaran et al. 2001), and their abundance has been associated with improved survival in CRC (Table 15) as well as in other solid tumors (Fridman et al. 2012). The results of these studies support their prognostic value, since CD3+ T cells at the IM, as well as FoxP3+ T cells at the IM and in the CT-S associated with improved DFS in 24month follow-up in Cohort 2a (II), and the density of CD3+ T cells predicted improved CSS in a subset of 235 patients of Cohort 1 independent of tumor stage (I). Different types of T cells had high positive correlations between each other and formed a group on the top of the dendrogram in the hierarchical cluster analysis (Fig. 11). 84 Some discrepancy exists about the significance of TReg cells in cancer. High TReg cell infiltration has been associated with poor survival in, e.g., ovarian (Curiel et al. 2004) and breast cancer (Bates et al. 2006), which is in accordance with the role of TReg cells in suppressing the immune responses (Zou, 2006). However, the results of these studies support the majority of the published results associating TReg cells with a favorable outcome in CRC (Table 16) since a high FoxP3+ T-cell count in the CT-S had the highest association with improved DFS of all the individual cell markers and FoxP3+ T cells clustered along with other T cells in hierarchical clustering analysis (Fig. 11) (II). The mechanisms accounting for the impact of TReg cells in CRC and for the inconsistencies in their roles in different cancers merit further research. 6.2.2 Dendritic cells in colorectal cancer DCs are important APCs responsible for the induction of adaptive immune responses (Banchereau et al. 2000). After capturing antigens, immature DCs mostly reside in lymph nodes to mature and present antigens to T cells. The significance of tumor-infiltrating mature DCs in CRC immunity was originally described by Suzuki et al. (2002), who found that mature DCs make small aggregates with T cells in the IM of CRC to promote T-cell activation. In agreement with this finding, high numbers of mature DCs were found both at the IM and in the CT-S (II). This suggests that after antigen capture, some of the DCs reside in tumor stroma, mature, and potentially contribute to T-cell activation. This phenomenon has also been reported to occur in other malignancies, such as non-small-cell lung cancer (Dieu-Nosjean et al. 2008). The results of this study indicate that CD1a+ immature DCs do not associate with tumor stage and they also clustered far apart from other cells in hierarchical clustering, whereas CD83 + mature DCs had a strong association with lower stage and clustered along with CD3+ T cells (Fig. 11). This result supports the importance of tumor-infiltrating mature DCs in effective T cell responses against the tumor and encourages further studies to address different DC subtypes in CRC. 6.2.3 Colorectal cancer associated lymphoid reaction Study III evaluated the significance of CLR by correlating CLR density with clinicopathological variables and the densities of tumor-infiltrating immune cells. Of all the analyzed cell types, the number of peritumoral CD83+ mature DCs had 85 the highest positive correlation with CLR density (III), suggesting an important role for mature DCs in the development of CLR or shared background factors between mature DCs and CLR. CLR density also notably correlated with the densities of T cells at the IM and in the CT. High CLR density was associated with low tumor stage, but also correlated with better survival regardless of stage. Moreover, high Ki-67 activity was observed in the germinal centers of CLR follicles, pointing out that it represents an area of immune cell proliferation. Taken together, these findings suggest that CLR contributes to the adaptive antitumor immunity along with T cells and mature DCs. Shortly after Study III was published, also another study evaluating the structure of CLR and its relation to tumor infiltrating T cells and patient survival was released (Di Caro et al. 2014). The authors added to the structural analysis that we conducted by demonstrating that the lymphoid follicles surrounding the tumors contained peripheral node addressin expressing high-endothelial venules (Di Caro et al. 2014), which have shown to enable circulating lymphocytes to directly enter the tissue (Aloisi & Pujol-Borrell 2006). This finding further enlightens the mechanisms of CLR formation. Moreover, utilizing a retrospective cohort of 351 stage II and III CRC patients, the authors confirmed our results of intensive CLR associating with increased numbers of tumor-infiltrating T cells and beneficial clinical outcome (Di Caro et al. 2014). The adaptive immune responses against the tumor are modulated by tumor immunogenicity (Buckowitz et al. 2005, Shankaran et al. 2001). Present in about 15% of CRC (Boland & Goel 2010), MSI induces the generation of novel tumorspecific frameshift peptides (Schwitalle et al. 2008), potentially increasing the immunogenicity. Accordingly, MMR deficiency associated with high CLR density (III) and high densities of CD3+ and CD8+ T cells (II). However, the prognostic value of CLR density was independent of MMR deficiency (III), suggesting that CLR likely also reflects several other tumor- and host-related factors accounting for tumor immunogenicity, potentially including, e.g., decreased HLA expression (Simpson et al. 2010). 6.2.4 Future perspectives There is a need to standardize the analysis of immune cell infiltration in CRC to provide a useful prognostic and potentially predictive tool (Galon et al. 2014, 2012). The stage-independent prognostic value of T cells has been convincingly demonstrated (Table 15), and there is also an international initiative to incorporate 86 Immunoscore into cancer classification (Galon et al. 2012, 2014). The results of these studies support the validity of CD3 and CD8 as representative inflammatory markers in CRC (I, II). Furthermore, the results propose that the computation of cell densities from multiple locations improves the prognostic value relative to the determination in one location (I). However, the Cox regression models (III) suggest that CD3 immunohistochemistry does not give any additional prognostic value compared with inflammatory reaction scoring from the H&E stained sections (Klintrup-Mäkinen score and CLR density). Also another study was recently published which indicated that Immunoscore and the Klintrup-Mäkinen score exhibit similar survival relationships (Richards et al. 2014). However, the evaluation of the Klintrup-Mäkinen score is subjective, while Immunoscore counting, CLR density calculation, and other evaluations based on cell or structure counting benefit from higher objectivity and continuous nature of gathered data. Therefore, these types of evaluations are more likely to yield reproducible information that can subsequently be applied into clinical decisionmaking in the Western world. Especially, ongoing large scale studies are expected to validate the clinical significance of the densities of tumor-infiltrating T cells (Galon et al. 2014). Conversely, the evaluation of immune cell infiltration from H&E slides has the advantage of lower cost and better availability of the methodology, which could be valuable for countries with no financial resources for immunohistochemical or molecular biological approaches. In future, the evaluation of immune cell infiltration has also potential to help to predict the response to the treatments in CRC and other malignancies (Ascierto et al. 2013). Ipilimumab — a monoclonal antibody against cytotoxic T lymphocyte associated antigen (CTLA-4), a protein receptor that downregulates the immune system — has recently been introduced in the treatment of metastatic melanoma with promising results (Hodi et al. 2010) and other immunomodulative treatments, including programmed death 1 (PD-1) antagonists (Hamid et al. 2013) and OX40 agonists (Curti et al. 2013), are currently evaluated in clinical trials. No predictive markers for ipilimumab treatment in melanoma have yet been established, although a recent study associated a high number of tumorinfiltrating FoxP3+ cells with a positive response to therapy (Hamid et al. 2011). A number of studies have also evaluated immunomodulative therapies in CRC but the results have been less promising than in melanoma. For example, in patients with metastatic CRC in whom standard treatments had failed, tremelimumab, a CTLA-4 monoclonal antibody, did not show clinically meaningful single-agent activity (Chung et al. 2010). However, extensive research on potential 87 immunomodulative therapies in CRC is ongoing, and because of its strong prognostic value, it is also under investigation whether the immune cell infiltrate predicts response to traditional adjuvant treatments in, e.g., stage II CRC patients (Galon et al. 2012). Interestingly, a recent study based on 55 patients indicated that the density of CD3+ and CD8+ T cells in the preoperative rectal cancer biopsies could predict the response to neoadjuvant CRT (Anitei et al. 2014). However, the results need to be validated before they can be applied into clinical practice. 6.3 Systemic inflammatory biomarkers in colorectal cancer In Study IV, it was established that the median serum MMP-8 level of CRC patients is more than three times higher than that of age- and sex-matched controls. Earlier, serum or plasma levels of MMP-8 have been shown to be elevated, e.g., in Helicobacter pylori gastritis (Rautelin et al. 2009) and several cardiovascular diseases (Pradhan-Palikhe et al. 2010, Tuomainen et al. 2007). MMP-8 plays a notable role in regulating inflammatory reactions. Produced mainly by neutrophils, it has been thought to contribute particularly to the acute inflammation (Van Lint & Libert 2006) by the cleavage of chemokines and cytokines such as CXCL5, CXCL8, CXCL9 and CCL2 to either inactivate them or increase their potency (Van Lint & Libert 2007). In a MMP-8 knockout mouse skin cancer experiment, Balbin et al. (2003) observed that one day after carcinogen injection, MMP-8-deficient mice developed a weaker and more diffuse neutrophil influx to the area of carcinogen injection than wild-type mice. However, 7 to 28 days after the injection, a sustained inflammatory response was observed in the MMP-8-deficient mice. This suggests that MMP-8 contributes to neutrophil recruitment during acute inflammation but is also involved in resolving chronic inflammation, which is supported by subsequent studies (Cox et al. 2010, Gutierrez-Fernandez et al. 2007). Based on this experimental data, it can be hypothesized that the association between lower serum MMP-8 and more intense CLR might stem from the proteolytic modification of chemokines and cytokines by MMP-8. During the progression of CRC, the excess of MMP-8 might help to resolve the immune response against the tumor, thus contributing to tumor escape from immunosurveillance. However, the hypothesis needs to be addressed by further studies with a more experimental study design. No peripheral blood markers are currently regularly used in the screening or diagnostics of CRC, while CEA is the marker most frequently used in the follow88 up (Sturgeon et al. 2008). In this study, serum MMP-8 achieved a good accuracy in separating CRC patients from healthy controls with an AUC of 0.751 in ROC analysis. However, the study has its limitations in this regard, because only the differences in serum MMP-8 between CRC patients and healthy age and gender matched controls were analyzed, instead of patients with gastrointestinal symptoms. Therefore, the potential value of serum MMP-8 in CRC screening, diagnostics or surveillance needs to be confirmed by subsequent studies. 89 90 7 Conclusions The present studies enlighten the significance of various immune cell types and inflammatory biomarkers in CRC. Based on the results, the following conclusions were made: 1. 2. 3. 4. 5. 6. Color layer separation based image analysis provides an accurate and reproducible method for counting immune cells in CRC. CLR density counting is a reproducible method for the evaluation of CLR. There are high positive correlations between the densities of tumor infiltrating CD3+, CD8+, and FoxP3+ T cells, CD83+ DCs, CD68+ macrophages, and neutrophils, whereas CD1a+ DCs and mast cells show weaker correlation with other cell types. High T cell density in the tumor samples predicts a favorable outcome in CRC. High CLR density correlates with low tumor stage, but also correlates with better survival regardless of stage, suggesting that it represents a relevant prognostic indicator in CRC. The numbers of tumor-infiltrating T cells correlate closely with the CLR density, suggesting that the CLR plays a role in adaptive antitumor immunity. 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BR[ 00 FI-90014 UNIVERSITY OF OULU FINLAND U N I V E R S I TAT I S S E R I E S SCIENTIAE RERUM NATURALIUM Professor Esa Hohtola HUMANIORA University Lecturer Santeri Palviainen TECHNICA Postdoctoral research fellow Sanna Taskila ACTA IMMUNE CELL INFILTRATION AND INFLAMMATORY BIOMARKERS IN COLORECTAL CANCER MEDICA Professor Olli Vuolteenaho SCIENTIAE RERUM SOCIALIUM University Lecturer Veli-Matti Ulvinen SCRIPTA ACADEMICA Director Sinikka Eskelinen OECONOMICA Professor Jari Juga EDITOR IN CHIEF Professor Olli Vuolteenaho PUBLICATIONS EDITOR Publications Editor Kirsti Nurkkala ISBN 978-952-62-0640-0 (Paperback) ISBN 978-952-62-0641-7 (PDF) ISSN 0355-3221 (Print) ISSN 1796-2234 (Online) U N I V E R S I T AT I S O U L U E N S I S Juha Väyrynen E D I T O R S Juha Väyrynen A B C D E F G O U L U E N S I S ACTA A C TA D 1269 UNIVERSITY OF OULU GRADUATE SCHOOL; UNIVERSITY OF OULU, FACULTY OF MEDICINE, INSTITUTE OF DIAGNOSTICS, DEPARTMENT OF PATHOLOGY; MEDICAL RESEARCH CENTER OULU; OULU UNIVERSITY HOSPITAL D MEDICA
